diff --git a/.github/actions/setup-poetry/action.yml b/.github/actions/setup-poetry/action.yml
index 0bdd730c..473326dc 100644
--- a/.github/actions/setup-poetry/action.yml
+++ b/.github/actions/setup-poetry/action.yml
@@ -8,7 +8,7 @@ runs:
   using: 'composite'
   steps:
     - name: Install poetry
-      run: pipx install poetry==1.8.3
+      run: pipx install poetry==1.8.5
       shell: bash
     - uses: actions/setup-python@v5
       with:
diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml
index 1cd08f2c..e04e2803 100644
--- a/.github/workflows/checks.yml
+++ b/.github/workflows/checks.yml
@@ -6,11 +6,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
     steps:
       - uses: actions/checkout@v4
       - name: Install tesseract
-        run: sudo apt-get update && sudo apt-get install -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-fra tesseract-ocr-deu tesseract-ocr-spa libleptonica-dev libtesseract-dev pkg-config
+        run: sudo apt-get update && sudo apt-get install -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-fra tesseract-ocr-deu tesseract-ocr-spa tesseract-ocr-script-latn libleptonica-dev libtesseract-dev pkg-config
       - name: Set TESSDATA_PREFIX
         run: |
           echo "TESSDATA_PREFIX=$(dpkg -L tesseract-ocr-eng | grep tessdata$)" >> "$GITHUB_ENV"
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 2733b522..0fc3ac7a 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -14,7 +14,10 @@ jobs:
         - uses: ./.github/actions/setup-poetry
         - name: Build docs
           run: poetry run mkdocs build --verbose --clean
+        - name: Make docs LLM ready
+          if: inputs.deploy
+          uses: demodrive-ai/llms-txt-action@ad720693843126e6a73910a667d0eba37c1dea4b
         - name: Build and push docs
           if: inputs.deploy
-          run: poetry run mkdocs gh-deploy --force
- 
\ No newline at end of file
+          run: poetry run mkdocs gh-deploy --force --dirty
+ 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 21882618..597dde6e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,49 @@
+## [v2.17.0](https://github.com/DS4SD/docling/releases/tag/v2.17.0) - 2025-01-28
+
+### Feature
+
+* **CLI:** Expose code and formula models in the CLI ([#820](https://github.com/DS4SD/docling/issues/820)) ([`6882e6c`](https://github.com/DS4SD/docling/commit/6882e6c38df30e4d4a1b83e01b13900ca7ea001f))
+* Add platform info to CLI version printout ([#816](https://github.com/DS4SD/docling/issues/816)) ([`95b293a`](https://github.com/DS4SD/docling/commit/95b293a72356f94c7076e3649be970c8a51121a3))
+* **ocr:** Expose `rec_keys_path` in RapidOcrOptions to support custom dictionaries ([#786](https://github.com/DS4SD/docling/issues/786)) ([`5332755`](https://github.com/DS4SD/docling/commit/53327552e83ced079ae50d8067ba7a8ce80cd9ad))
+* Introduce automatic language detection in TesseractOcrCliModel ([#800](https://github.com/DS4SD/docling/issues/800)) ([`3be2fb5`](https://github.com/DS4SD/docling/commit/3be2fb581fe5a2ebd5cec9c86bb22eb1dec6fd0f))
+
+### Fix
+
+* Fix single newline handling in MD backend ([#824](https://github.com/DS4SD/docling/issues/824)) ([`5aed9f8`](https://github.com/DS4SD/docling/commit/5aed9f8aeba1624ba1a721e2ed3ba4aceaa7a482))
+* Use file extension if filetype fails with PDF ([#827](https://github.com/DS4SD/docling/issues/827)) ([`adf6353`](https://github.com/DS4SD/docling/commit/adf635348365f82daa64e3f879076a7baf71edc0))
+* Parse html with omitted body tag ([#818](https://github.com/DS4SD/docling/issues/818)) ([`a112d7a`](https://github.com/DS4SD/docling/commit/a112d7a03512e8a00842a100416426254d6ecfc0))
+
+### Documentation
+
+* Document Docling JSON parsing ([#819](https://github.com/DS4SD/docling/issues/819)) ([`6875913`](https://github.com/DS4SD/docling/commit/6875913e34abacb8d71b5d31543adbf7b5bd5e92))
+* Add SSL verification error mitigation ([#821](https://github.com/DS4SD/docling/issues/821)) ([`5139b48`](https://github.com/DS4SD/docling/commit/5139b48e4e62bb061d956c132958ec2e6d88e40a))
+* **backend XML:** Do not delete temp file in notebook ([#817](https://github.com/DS4SD/docling/issues/817)) ([`4d41db3`](https://github.com/DS4SD/docling/commit/4d41db3f7abb86c8c65386bf94e7eb0bf22bb82b))
+* Typo ([#814](https://github.com/DS4SD/docling/issues/814)) ([`8a4ec77`](https://github.com/DS4SD/docling/commit/8a4ec77576b8a9fd60d0047939665d00cf93b4dd))
+* Added markdown headings to enable TOC in github pages ([#808](https://github.com/DS4SD/docling/issues/808)) ([`b885b2f`](https://github.com/DS4SD/docling/commit/b885b2fa3c2519c399ed4b9a3dd4c2f6f62235d1))
+* Description of supported formats and backends ([#788](https://github.com/DS4SD/docling/issues/788)) ([`c2ae1cc`](https://github.com/DS4SD/docling/commit/c2ae1cc4cab0f9e693c7ca460fe8afa5b515ee94))
+
+## [v2.16.0](https://github.com/DS4SD/docling/releases/tag/v2.16.0) - 2025-01-24
+
+### Feature
+
+* New document picture classifier ([#805](https://github.com/DS4SD/docling/issues/805)) ([`16a218d`](https://github.com/DS4SD/docling/commit/16a218d871c48fd9cc636b77f7b597dc40cbeeec))
+* Add Docling JSON ingestion ([#783](https://github.com/DS4SD/docling/issues/783)) ([`88a0e66`](https://github.com/DS4SD/docling/commit/88a0e66adc19238f57a942b0504926cdaeacd8cc))
+* Code and equation model for PDF and code blocks in markdown ([#752](https://github.com/DS4SD/docling/issues/752)) ([`3213b24`](https://github.com/DS4SD/docling/commit/3213b247ad6870ff984271f09f7720be68d9479b))
+* Add "auto" language for TesseractOcr ([#759](https://github.com/DS4SD/docling/issues/759)) ([`8543c22`](https://github.com/DS4SD/docling/commit/8543c22687fee40459d393bf4adcfc059712de02))
+
+### Fix
+
+* Added extraction of byte-images in excel ([#804](https://github.com/DS4SD/docling/issues/804)) ([`a458e29`](https://github.com/DS4SD/docling/commit/a458e298ca64da2c6df29d953e95645525817bed))
+* Update docling-parse-v2 backend version with new parsing fixes ([#769](https://github.com/DS4SD/docling/issues/769)) ([`670a08b`](https://github.com/DS4SD/docling/commit/670a08bdedda847ff3b6942bcaa1a2adef79afe2))
+
+### Documentation
+
+* Fix minor typos ([#801](https://github.com/DS4SD/docling/issues/801)) ([`c58f75d`](https://github.com/DS4SD/docling/commit/c58f75d0f75040e32820cc2915ec00755211c02f))
+* Add Azure RAG example ([#675](https://github.com/DS4SD/docling/issues/675)) ([`9020a93`](https://github.com/DS4SD/docling/commit/9020a934be35b0798c972eb77a22fb62ce654ca5))
+* Fix links between docs pages ([#697](https://github.com/DS4SD/docling/issues/697)) ([`c49b352`](https://github.com/DS4SD/docling/commit/c49b3526fb7b72e8007f785b1fcfdf58c2457756))
+* Fix correct Accelerator pipeline options in docs/examples/custom_convert.py ([#733](https://github.com/DS4SD/docling/issues/733)) ([`7686083`](https://github.com/DS4SD/docling/commit/768608351d40376c3504546f52e967195536b3d5))
+* Example to translate documents ([#739](https://github.com/DS4SD/docling/issues/739)) ([`f7e1cbf`](https://github.com/DS4SD/docling/commit/f7e1cbf629ae5f3e279296e72f656b7a453ab7a3))
+
 ## [v2.15.1](https://github.com/DS4SD/docling/releases/tag/v2.15.1) - 2025-01-10
 
 ### Fix
diff --git a/README.md b/README.md
index 78acb592..5a957d60 100644
--- a/README.md
+++ b/README.md
@@ -22,23 +22,25 @@
 [![License MIT](https://img.shields.io/github/license/DS4SD/docling)](https://opensource.org/licenses/MIT)
 [![PyPI Downloads](https://static.pepy.tech/badge/docling/month)](https://pepy.tech/projects/docling)
 
-Docling parses documents and exports them to the desired format with ease and speed.
+Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
 
 ## Features
 
-* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
-* 📑 Advanced PDF document understanding including page layout, reading order & table structures
-* 🧩 Unified, expressive [DoclingDocument](https://ds4sd.github.io/docling/concepts/docling_document/) representation format
-* 🤖 Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
-* 🔍 OCR support for scanned PDFs
+* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, XLSX, HTML, images, and more
+* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
+* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
+* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, and lossless JSON
+* 🔒 Local execution capabilities for sensitive data and air-gapped environments
+* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
+* 🔍 Extensive OCR support for scanned PDFs and images
 * 💻 Simple and convenient CLI
 
-Explore the [documentation](https://ds4sd.github.io/docling/) to discover plenty examples and unlock the full power of Docling!
-
 ### Coming soon
 
-* ♾️ Equation & code extraction
 * 📝 Metadata extraction, including title, authors, references & language
+* 📝 Inclusion of Visual Language Models ([SmolDocling](https://huggingface.co/blog/smolervlm#smoldocling))
+* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
+* 📝 Complex chemistry understanding (Molecular structures)
 
 ## Installation
 
@@ -120,3 +122,7 @@ For individual model usage, please refer to the model licenses found in the orig
 ## IBM ❤️ Open Source AI
 
 Docling has been brought to you by IBM.
+
+[supported_formats]: https://ds4sd.github.io/docling/supported_formats/
+[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
+[integrations]: https://ds4sd.github.io/docling/integrations/
diff --git a/docling/backend/abstract_backend.py b/docling/backend/abstract_backend.py
index b47b11cd..491330b3 100644
--- a/docling/backend/abstract_backend.py
+++ b/docling/backend/abstract_backend.py
@@ -27,7 +27,6 @@ class AbstractDocumentBackend(ABC):
     def supports_pagination(cls) -> bool:
         pass
 
-    @abstractmethod
     def unload(self):
         if isinstance(self.path_or_stream, BytesIO):
             self.path_or_stream.close()
diff --git a/docling/backend/asciidoc_backend.py b/docling/backend/asciidoc_backend.py
index 829419af..397bfc44 100644
--- a/docling/backend/asciidoc_backend.py
+++ b/docling/backend/asciidoc_backend.py
@@ -24,7 +24,6 @@ _log = logging.getLogger(__name__)
 
 
 class AsciiDocBackend(DeclarativeDocumentBackend):
-
     def __init__(self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path]):
         super().__init__(in_doc, path_or_stream)
 
diff --git a/docling/backend/docling_parse_backend.py b/docling/backend/docling_parse_backend.py
index 89b25ee1..6d22127b 100644
--- a/docling/backend/docling_parse_backend.py
+++ b/docling/backend/docling_parse_backend.py
@@ -163,7 +163,7 @@ class DoclingParsePageBackend(PdfPageBackend):
                 l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
             )
         else:
-            padbox = cropbox.to_bottom_left_origin(page_size.height)
+            padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
             padbox.r = page_size.width - padbox.r
             padbox.t = page_size.height - padbox.t
 
diff --git a/docling/backend/docling_parse_v2_backend.py b/docling/backend/docling_parse_v2_backend.py
index 366fa6ac..27a368f9 100644
--- a/docling/backend/docling_parse_v2_backend.py
+++ b/docling/backend/docling_parse_v2_backend.py
@@ -178,7 +178,7 @@ class DoclingParseV2PageBackend(PdfPageBackend):
                 l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
             )
         else:
-            padbox = cropbox.to_bottom_left_origin(page_size.height)
+            padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
             padbox.r = page_size.width - padbox.r
             padbox.t = page_size.height - padbox.t
 
diff --git a/docling/backend/html_backend.py b/docling/backend/html_backend.py
index ae478885..286dfbfa 100644
--- a/docling/backend/html_backend.py
+++ b/docling/backend/html_backend.py
@@ -1,9 +1,9 @@
 import logging
 from io import BytesIO
 from pathlib import Path
-from typing import Set, Union
+from typing import Optional, Set, Union
 
-from bs4 import BeautifulSoup
+from bs4 import BeautifulSoup, Tag
 from docling_core.types.doc import (
     DocItemLabel,
     DoclingDocument,
@@ -24,7 +24,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
     def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
         super().__init__(in_doc, path_or_stream)
         _log.debug("About to init HTML backend...")
-        self.soup = None
+        self.soup: Optional[Tag] = None
         # HTML file:
         self.path_or_stream = path_or_stream
         # Initialise the parents for the hierarchy
@@ -78,17 +78,18 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
 
         if self.is_valid():
             assert self.soup is not None
+            content = self.soup.body or self.soup
             # Replace <br> tags with newline characters
-            for br in self.soup.body.find_all("br"):
+            for br in content.find_all("br"):
                 br.replace_with("\n")
-            doc = self.walk(self.soup.body, doc)
+            doc = self.walk(content, doc)
         else:
             raise RuntimeError(
                 f"Cannot convert doc with {self.document_hash} because the backend failed to init."
             )
         return doc
 
-    def walk(self, element, doc):
+    def walk(self, element: Tag, doc: DoclingDocument):
         try:
             # Iterate over elements in the body of the document
             for idx, element in enumerate(element.children):
@@ -105,7 +106,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
 
         return doc
 
-    def analyse_element(self, element, idx, doc):
+    def analyse_element(self, element: Tag, idx: int, doc: DoclingDocument):
         """
         if element.name!=None:
             _log.debug("\t"*self.level, idx, "\t", f"{element.name} ({self.level})")
@@ -135,7 +136,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
         else:
             self.walk(element, doc)
 
-    def get_direct_text(self, item):
+    def get_direct_text(self, item: Tag):
         """Get the direct text of the <li> element (ignoring nested lists)."""
         text = item.find(string=True, recursive=False)
         if isinstance(text, str):
@@ -144,7 +145,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
         return ""
 
     # Function to recursively extract text from all child nodes
-    def extract_text_recursively(self, item):
+    def extract_text_recursively(self, item: Tag):
         result = []
 
         if isinstance(item, str):
@@ -165,7 +166,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
 
         return "".join(result) + " "
 
-    def handle_header(self, element, idx, doc):
+    def handle_header(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles header tags (h1, h2, etc.)."""
         hlevel = int(element.name.replace("h", ""))
         slevel = hlevel - 1
@@ -207,7 +208,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
                 level=hlevel,
             )
 
-    def handle_code(self, element, idx, doc):
+    def handle_code(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles monospace code snippets (pre)."""
         if element.text is None:
             return
@@ -215,9 +216,9 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
         label = DocItemLabel.CODE
         if len(text) == 0:
             return
-        doc.add_text(parent=self.parents[self.level], label=label, text=text)
+        doc.add_code(parent=self.parents[self.level], text=text)
 
-    def handle_paragraph(self, element, idx, doc):
+    def handle_paragraph(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles paragraph tags (p)."""
         if element.text is None:
             return
@@ -227,7 +228,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
             return
         doc.add_text(parent=self.parents[self.level], label=label, text=text)
 
-    def handle_list(self, element, idx, doc):
+    def handle_list(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles list tags (ul, ol) and their list items."""
 
         if element.name == "ul":
@@ -249,7 +250,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
         self.parents[self.level + 1] = None
         self.level -= 1
 
-    def handle_listitem(self, element, idx, doc):
+    def handle_listitem(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles listitem tags (li)."""
         nested_lists = element.find(["ul", "ol"])
 
@@ -303,7 +304,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
         else:
             _log.warn("list-item has no text: ", element)
 
-    def handle_table(self, element, idx, doc):
+    def handle_table(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles table tags."""
 
         nested_tables = element.find("table")
@@ -376,7 +377,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
 
         doc.add_table(data=data, parent=self.parents[self.level])
 
-    def get_list_text(self, list_element, level=0):
+    def get_list_text(self, list_element: Tag, level=0):
         """Recursively extract text from <ul> or <ol> with proper indentation."""
         result = []
         bullet_char = "*"  # Default bullet character for unordered lists
@@ -402,7 +403,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
 
         return result
 
-    def extract_table_cell_text(self, cell):
+    def extract_table_cell_text(self, cell: Tag):
         """Extract text from a table cell, including lists with indents."""
         contains_lists = cell.find(["ul", "ol"])
         if contains_lists is None:
@@ -413,7 +414,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
             )
             return cell.text
 
-    def handle_figure(self, element, idx, doc):
+    def handle_figure(self, element: Tag, idx: int, doc: DoclingDocument):
         """Handles image tags (img)."""
 
         # Extract the image URI from the <img> tag
@@ -436,6 +437,6 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
                 caption=fig_caption,
             )
 
-    def handle_image(self, element, idx, doc):
+    def handle_image(self, element: Tag, idx, doc: DoclingDocument):
         """Handles image tags (img)."""
         doc.add_picture(parent=self.parents[self.level], caption=None)
diff --git a/docling/backend/json/__init__.py b/docling/backend/json/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/docling/backend/json/docling_json_backend.py b/docling/backend/json/docling_json_backend.py
new file mode 100644
index 00000000..73ac6972
--- /dev/null
+++ b/docling/backend/json/docling_json_backend.py
@@ -0,0 +1,58 @@
+from io import BytesIO
+from pathlib import Path
+from typing import Union
+
+from docling_core.types.doc import DoclingDocument
+from typing_extensions import override
+
+from docling.backend.abstract_backend import DeclarativeDocumentBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import InputDocument
+
+
+class DoclingJSONBackend(DeclarativeDocumentBackend):
+    @override
+    def __init__(
+        self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path]
+    ) -> None:
+        super().__init__(in_doc, path_or_stream)
+
+        # given we need to store any actual conversion exception for raising it from
+        # convert(), this captures the successful result or the actual error in a
+        # mutually exclusive way:
+        self._doc_or_err = self._get_doc_or_err()
+
+    @override
+    def is_valid(self) -> bool:
+        return isinstance(self._doc_or_err, DoclingDocument)
+
+    @classmethod
+    @override
+    def supports_pagination(cls) -> bool:
+        return False
+
+    @classmethod
+    @override
+    def supported_formats(cls) -> set[InputFormat]:
+        return {InputFormat.JSON_DOCLING}
+
+    def _get_doc_or_err(self) -> Union[DoclingDocument, Exception]:
+        try:
+            json_data: Union[str, bytes]
+            if isinstance(self.path_or_stream, Path):
+                with open(self.path_or_stream, encoding="utf-8") as f:
+                    json_data = f.read()
+            elif isinstance(self.path_or_stream, BytesIO):
+                json_data = self.path_or_stream.getvalue()
+            else:
+                raise RuntimeError(f"Unexpected: {type(self.path_or_stream)=}")
+            return DoclingDocument.model_validate_json(json_data=json_data)
+        except Exception as e:
+            return e
+
+    @override
+    def convert(self) -> DoclingDocument:
+        if isinstance(self._doc_or_err, DoclingDocument):
+            return self._doc_or_err
+        else:
+            raise self._doc_or_err
diff --git a/docling/backend/md_backend.py b/docling/backend/md_backend.py
index 2bcc6d7d..669096eb 100644
--- a/docling/backend/md_backend.py
+++ b/docling/backend/md_backend.py
@@ -3,32 +3,40 @@ import re
 import warnings
 from io import BytesIO
 from pathlib import Path
-from typing import Set, Union
+from typing import List, Optional, Set, Union
 
 import marko
+import marko.element
 import marko.ext
 import marko.ext.gfm
 import marko.inline
 from docling_core.types.doc import (
+    DocItem,
     DocItemLabel,
     DoclingDocument,
     DocumentOrigin,
     GroupLabel,
+    NodeItem,
     TableCell,
     TableData,
+    TextItem,
 )
 from marko import Markdown
 
 from docling.backend.abstract_backend import DeclarativeDocumentBackend
+from docling.backend.html_backend import HTMLDocumentBackend
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import InputDocument
 
 _log = logging.getLogger(__name__)
 
+_MARKER_BODY = "DOCLING_DOC_MD_HTML_EXPORT"
+_START_MARKER = f"#_#_{_MARKER_BODY}_START_#_#"
+_STOP_MARKER = f"#_#_{_MARKER_BODY}_STOP_#_#"
+
 
 class MarkdownDocumentBackend(DeclarativeDocumentBackend):
-
-    def shorten_underscore_sequences(self, markdown_text, max_length=10):
+    def shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10):
         # This regex will match any sequence of underscores
         pattern = r"_+"
 
@@ -63,7 +71,8 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
 
         self.in_table = False
         self.md_table_buffer: list[str] = []
-        self.inline_text_buffer = ""
+        self.inline_texts: list[str] = []
+        self._html_blocks: int = 0
 
         try:
             if isinstance(self.path_or_stream, BytesIO):
@@ -90,13 +99,13 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
             ) from e
         return
 
-    def close_table(self, doc=None):
+    def close_table(self, doc: DoclingDocument):
         if self.in_table:
             _log.debug("=== TABLE START ===")
             for md_table_row in self.md_table_buffer:
                 _log.debug(md_table_row)
             _log.debug("=== TABLE END ===")
-            tcells = []
+            tcells: List[TableCell] = []
             result_table = []
             for n, md_table_row in enumerate(self.md_table_buffer):
                 data = []
@@ -137,33 +146,42 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
             self.in_table = False
             self.md_table_buffer = []  # clean table markdown buffer
             # Initialize Docling TableData
-            data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=tcells)
+            table_data = TableData(
+                num_rows=num_rows, num_cols=num_cols, table_cells=tcells
+            )
             # Populate
             for tcell in tcells:
-                data.table_cells.append(tcell)
+                table_data.table_cells.append(tcell)
             if len(tcells) > 0:
-                doc.add_table(data=data)
+                doc.add_table(data=table_data)
         return
 
-    def process_inline_text(self, parent_element, doc=None):
-        # self.inline_text_buffer += str(text_in)
-        txt = self.inline_text_buffer.strip()
+    def process_inline_text(
+        self, parent_element: Optional[NodeItem], doc: DoclingDocument
+    ):
+        txt = " ".join(self.inline_texts)
         if len(txt) > 0:
             doc.add_text(
                 label=DocItemLabel.PARAGRAPH,
                 parent=parent_element,
                 text=txt,
             )
-        self.inline_text_buffer = ""
+        self.inline_texts = []
 
-    def iterate_elements(self, element, depth=0, doc=None, parent_element=None):
+    def iterate_elements(
+        self,
+        element: marko.element.Element,
+        depth: int,
+        doc: DoclingDocument,
+        parent_element: Optional[NodeItem] = None,
+    ):
         # Iterates over all elements in the AST
         # Check for different element types and process relevant details
-        if isinstance(element, marko.block.Heading):
+        if isinstance(element, marko.block.Heading) and len(element.children) > 0:
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(
-                f" - Heading level {element.level}, content: {element.children[0].children}"
+                f" - Heading level {element.level}, content: {element.children[0].children}"  # type: ignore
             )
             if element.level == 1:
                 doc_label = DocItemLabel.TITLE
@@ -172,10 +190,10 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
 
             # Header could have arbitrary inclusion of bold, italic or emphasis,
             # hence we need to traverse the tree to get full text of a header
-            strings = []
+            strings: List[str] = []
 
             # Define a recursive function to traverse the tree
-            def traverse(node):
+            def traverse(node: marko.block.BlockElement):
                 # Check if the node has a "children" attribute
                 if hasattr(node, "children"):
                     # If "children" is a list, continue traversal
@@ -194,24 +212,33 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
                 )
 
         elif isinstance(element, marko.block.List):
+            has_non_empty_list_items = False
+            for child in element.children:
+                if isinstance(child, marko.block.ListItem) and len(child.children) > 0:
+                    has_non_empty_list_items = True
+                    break
+
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(f" - List {'ordered' if element.ordered else 'unordered'}")
-            list_label = GroupLabel.LIST
-            if element.ordered:
-                list_label = GroupLabel.ORDERED_LIST
-            parent_element = doc.add_group(
-                label=list_label, name=f"list", parent=parent_element
-            )
+            if has_non_empty_list_items:
+                label = GroupLabel.ORDERED_LIST if element.ordered else GroupLabel.LIST
+                parent_element = doc.add_group(
+                    label=label, name=f"list", parent=parent_element
+                )
 
-        elif isinstance(element, marko.block.ListItem):
+        elif isinstance(element, marko.block.ListItem) and len(element.children) > 0:
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(" - List item")
 
-            snippet_text = str(element.children[0].children[0].children)
+            snippet_text = str(element.children[0].children[0].children)  # type: ignore
             is_numbered = False
-            if parent_element.label == GroupLabel.ORDERED_LIST:
+            if (
+                parent_element is not None
+                and isinstance(parent_element, DocItem)
+                and parent_element.label == GroupLabel.ORDERED_LIST
+            ):
                 is_numbered = True
             doc.add_list_item(
                 enumerated=is_numbered, parent=parent_element, text=snippet_text
@@ -221,89 +248,91 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(f" - Image with alt: {element.title}, url: {element.dest}")
-            doc.add_picture(parent=parent_element, caption=element.title)
 
-        elif isinstance(element, marko.block.Paragraph):
+            fig_caption: Optional[TextItem] = None
+            if element.title is not None and element.title != "":
+                fig_caption = doc.add_text(
+                    label=DocItemLabel.CAPTION, text=element.title
+                )
+
+            doc.add_picture(parent=parent_element, caption=fig_caption)
+
+        elif isinstance(element, marko.block.Paragraph) and len(element.children) > 0:
             self.process_inline_text(parent_element, doc)
 
         elif isinstance(element, marko.inline.RawText):
             _log.debug(f" - Paragraph (raw text): {element.children}")
-            snippet_text = str(element.children).strip()
+            snippet_text = element.children.strip()
             # Detect start of the table:
             if "|" in snippet_text:
                 # most likely part of the markdown table
                 self.in_table = True
                 if len(self.md_table_buffer) > 0:
-                    self.md_table_buffer[len(self.md_table_buffer) - 1] += str(
-                        snippet_text
-                    )
+                    self.md_table_buffer[len(self.md_table_buffer) - 1] += snippet_text
                 else:
                     self.md_table_buffer.append(snippet_text)
             else:
                 self.close_table(doc)
                 self.in_table = False
                 # most likely just inline text
-                self.inline_text_buffer += str(
-                    element.children
-                )  # do not strip an inline text, as it may contain important spaces
+                self.inline_texts.append(str(element.children))
 
         elif isinstance(element, marko.inline.CodeSpan):
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(f" - Code Span: {element.children}")
             snippet_text = str(element.children).strip()
-            doc.add_text(
-                label=DocItemLabel.CODE, parent=parent_element, text=snippet_text
-            )
+            doc.add_code(parent=parent_element, text=snippet_text)
 
-        elif isinstance(element, marko.block.CodeBlock):
+        elif (
+            isinstance(element, (marko.block.CodeBlock, marko.block.FencedCode))
+            and len(element.children) > 0
+            and isinstance((first_child := element.children[0]), marko.inline.RawText)
+            and len(snippet_text := (first_child.children.strip())) > 0
+        ):
             self.close_table(doc)
             self.process_inline_text(parent_element, doc)
             _log.debug(f" - Code Block: {element.children}")
-            snippet_text = str(element.children[0].children).strip()
-            doc.add_text(
-                label=DocItemLabel.CODE, parent=parent_element, text=snippet_text
-            )
-
-        elif isinstance(element, marko.block.FencedCode):
-            self.close_table(doc)
-            self.process_inline_text(parent_element, doc)
-            _log.debug(f" - Code Block: {element.children}")
-            snippet_text = str(element.children[0].children).strip()
-            doc.add_text(
-                label=DocItemLabel.CODE, parent=parent_element, text=snippet_text
-            )
+            doc.add_code(parent=parent_element, text=snippet_text)
 
         elif isinstance(element, marko.inline.LineBreak):
-            self.process_inline_text(parent_element, doc)
             if self.in_table:
                 _log.debug("Line break in a table")
                 self.md_table_buffer.append("")
 
         elif isinstance(element, marko.block.HTMLBlock):
+            self._html_blocks += 1
             self.process_inline_text(parent_element, doc)
             self.close_table(doc)
             _log.debug("HTML Block: {}".format(element))
             if (
-                len(element.children) > 0
+                len(element.body) > 0
             ):  # If Marko doesn't return any content for HTML block, skip it
-                snippet_text = str(element.children).strip()
-                doc.add_text(
-                    label=DocItemLabel.CODE, parent=parent_element, text=snippet_text
-                )
+                html_block = element.body.strip()
+
+                # wrap in markers to enable post-processing in convert()
+                text_to_add = f"{_START_MARKER}{html_block}{_STOP_MARKER}"
+                doc.add_code(parent=parent_element, text=text_to_add)
         else:
             if not isinstance(element, str):
                 self.close_table(doc)
                 _log.debug("Some other element: {}".format(element))
 
+        processed_block_types = (
+            marko.block.ListItem,
+            marko.block.Heading,
+            marko.block.CodeBlock,
+            marko.block.FencedCode,
+            # marko.block.Paragraph,
+            marko.inline.RawText,
+        )
+
         # Iterate through the element's children (if any)
-        if not isinstance(element, marko.block.ListItem):
-            if not isinstance(element, marko.block.Heading):
-                if not isinstance(element, marko.block.FencedCode):
-                    # if not isinstance(element, marko.block.Paragraph):
-                    if hasattr(element, "children"):
-                        for child in element.children:
-                            self.iterate_elements(child, depth + 1, doc, parent_element)
+        if hasattr(element, "children") and not isinstance(
+            element, processed_block_types
+        ):
+            for child in element.children:
+                self.iterate_elements(child, depth + 1, doc, parent_element)
 
     def is_valid(self) -> bool:
         return self.valid
@@ -339,6 +368,42 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
             # Start iterating from the root of the AST
             self.iterate_elements(parsed_ast, 0, doc, None)
             self.process_inline_text(None, doc)  # handle last hanging inline text
+
+            # if HTML blocks were detected, export to HTML and delegate to HTML backend
+            if self._html_blocks > 0:
+
+                # export to HTML
+                html_backend_cls = HTMLDocumentBackend
+                html_str = doc.export_to_html()
+
+                def _restore_original_html(txt, regex):
+                    _txt, count = re.subn(regex, "", txt)
+                    if count != self._html_blocks:
+                        raise RuntimeError(
+                            "An internal error has occurred during Markdown conversion."
+                        )
+                    return _txt
+
+                # restore original HTML by removing previouly added markers
+                for regex in [
+                    rf"<pre>\s*<code>\s*{_START_MARKER}",
+                    rf"{_STOP_MARKER}\s*</code>\s*</pre>",
+                ]:
+                    html_str = _restore_original_html(txt=html_str, regex=regex)
+                self._html_blocks = 0
+
+                # delegate to HTML backend
+                stream = BytesIO(bytes(html_str, encoding="utf-8"))
+                in_doc = InputDocument(
+                    path_or_stream=stream,
+                    format=InputFormat.HTML,
+                    backend=html_backend_cls,
+                    filename=self.file.name,
+                )
+                html_backend_obj = html_backend_cls(
+                    in_doc=in_doc, path_or_stream=stream
+                )
+                doc = html_backend_obj.convert()
         else:
             raise RuntimeError(
                 f"Cannot convert md with {self.document_hash} because the backend failed to init."
diff --git a/docling/backend/msexcel_backend.py b/docling/backend/msexcel_backend.py
index 508b0e8d..19c25341 100644
--- a/docling/backend/msexcel_backend.py
+++ b/docling/backend/msexcel_backend.py
@@ -26,6 +26,7 @@ _log = logging.getLogger(__name__)
 
 from typing import Any, List
 
+from PIL import Image as PILImage
 from pydantic import BaseModel
 
 
@@ -44,7 +45,6 @@ class ExcelTable(BaseModel):
 
 
 class MsExcelDocumentBackend(DeclarativeDocumentBackend):
-
     def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
         super().__init__(in_doc, path_or_stream)
 
@@ -326,49 +326,61 @@ class MsExcelDocumentBackend(DeclarativeDocumentBackend):
         self, doc: DoclingDocument, sheet: Worksheet
     ) -> DoclingDocument:
 
-        # FIXME: mypy does not agree with _images ...
+        # Iterate over byte images in the sheet
+        for idx, image in enumerate(sheet._images):  # type: ignore
+
+            try:
+                pil_image = PILImage.open(image.ref)
+
+                doc.add_picture(
+                    parent=self.parents[0],
+                    image=ImageRef.from_pil(image=pil_image, dpi=72),
+                    caption=None,
+                )
+            except:
+                _log.error("could not extract the image from excel sheets")
+
         """
-        # Iterate over images in the sheet
-        for idx, image in enumerate(sheet._images):  # Access embedded images
+        for idx, chart in enumerate(sheet._charts):  # type: ignore
+            try:
+                chart_path = f"chart_{idx + 1}.png"
+                _log.info(
+                    f"Chart found, but dynamic rendering is required for: {chart_path}"
+                )
 
-            image_bytes = BytesIO(image.ref.blob)
-            pil_image = Image.open(image_bytes)
-
-            doc.add_picture(
-                parent=self.parents[0],
-                image=ImageRef.from_pil(image=pil_image, dpi=72),
-                caption=None,
-            )
-        """
-
-        # FIXME: mypy does not agree with _charts ...
-        """
-        for idx, chart in enumerate(sheet._charts):  # Access embedded charts
-            chart_path = f"chart_{idx + 1}.png"
-            _log.info(
-                f"Chart found, but dynamic rendering is required for: {chart_path}"
-            )
-
-            _log.info(f"Chart {idx + 1}:")
-        
-            # Chart type
-            _log.info(f"Type: {type(chart).__name__}")
-            
-            # Title
-            if chart.title:
-                _log.info(f"Title: {chart.title}")
-            else:
-                _log.info("No title")
-            
-            # Data series
-            for series in chart.series:
-                _log.info(" => series ...")
-                _log.info(f"Data Series: {series.title}")
-                _log.info(f"Values: {series.values}")
-                _log.info(f"Categories: {series.categories}")
+                _log.info(f"Chart {idx + 1}:")
                 
-            # Position
-            # _log.info(f"Anchor Cell: {chart.anchor}")
+                # Chart type
+                # _log.info(f"Type: {type(chart).__name__}")
+                print(f"Type: {type(chart).__name__}")
+
+                # Extract series data
+                for series_idx, series in enumerate(chart.series):
+                    #_log.info(f"Series {series_idx + 1}:")
+                    print(f"Series {series_idx + 1} type: {type(series).__name__}")
+                    #print(f"x-values: {series.xVal}")
+                    #print(f"y-values: {series.yVal}")
+
+                    print(f"xval type: {type(series.xVal).__name__}")
+                    
+                    xvals = []
+                    for _ in series.xVal.numLit.pt:
+                        print(f"xval type: {type(_).__name__}")
+                        if hasattr(_, 'v'):
+                            xvals.append(_.v)
+
+                    print(f"x-values: {xvals}")
+                            
+                    yvals = []
+                    for _ in series.yVal:
+                        if hasattr(_, 'v'):
+                            yvals.append(_.v)
+                            
+                    print(f"y-values: {yvals}")                    
+                    
+            except Exception as exc:
+                print(exc)
+                continue
         """
 
         return doc
diff --git a/docling/backend/mspowerpoint_backend.py b/docling/backend/mspowerpoint_backend.py
index 995969d4..8b86008b 100644
--- a/docling/backend/mspowerpoint_backend.py
+++ b/docling/backend/mspowerpoint_backend.py
@@ -98,21 +98,28 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
 
         return doc
 
-    def generate_prov(self, shape, slide_ind, text=""):
-        left = shape.left
-        top = shape.top
-        width = shape.width
-        height = shape.height
+    def generate_prov(
+        self, shape, slide_ind, text="", slide_size=Size(width=1, height=1)
+    ):
+        if shape.left:
+            left = shape.left
+            top = shape.top
+            width = shape.width
+            height = shape.height
+        else:
+            left = 0
+            top = 0
+            width = slide_size.width
+            height = slide_size.height
         shape_bbox = [left, top, left + width, top + height]
         shape_bbox = BoundingBox.from_tuple(shape_bbox, origin=CoordOrigin.BOTTOMLEFT)
-        # prov = [{"bbox": shape_bbox, "page": parent_slide, "span": [0, len(text)]}]
         prov = ProvenanceItem(
             page_no=slide_ind + 1, charspan=[0, len(text)], bbox=shape_bbox
         )
 
         return prov
 
-    def handle_text_elements(self, shape, parent_slide, slide_ind, doc):
+    def handle_text_elements(self, shape, parent_slide, slide_ind, doc, slide_size):
         is_a_list = False
         is_list_group_created = False
         enum_list_item_value = 0
@@ -121,7 +128,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
         list_text = ""
         list_label = GroupLabel.LIST
         doc_label = DocItemLabel.LIST_ITEM
-        prov = self.generate_prov(shape, slide_ind, shape.text.strip())
+        prov = self.generate_prov(shape, slide_ind, shape.text.strip(), slide_size)
 
         # Identify if shape contains lists
         for paragraph in shape.text_frame.paragraphs:
@@ -270,18 +277,17 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
                 )
         return
 
-    def handle_pictures(self, shape, parent_slide, slide_ind, doc):
-        # Get the image bytes
-        image = shape.image
-        image_bytes = image.blob
-        im_dpi, _ = image.dpi
-
+    def handle_pictures(self, shape, parent_slide, slide_ind, doc, slide_size):
         # Open it with PIL
         try:
+            # Get the image bytes
+            image = shape.image
+            image_bytes = image.blob
+            im_dpi, _ = image.dpi
             pil_image = Image.open(BytesIO(image_bytes))
 
             # shape has picture
-            prov = self.generate_prov(shape, slide_ind, "")
+            prov = self.generate_prov(shape, slide_ind, "", slide_size)
             doc.add_picture(
                 parent=parent_slide,
                 image=ImageRef.from_pil(image=pil_image, dpi=im_dpi),
@@ -292,13 +298,13 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
             _log.warning(f"Warning: image cannot be loaded by Pillow: {e}")
         return
 
-    def handle_tables(self, shape, parent_slide, slide_ind, doc):
+    def handle_tables(self, shape, parent_slide, slide_ind, doc, slide_size):
         # Handling tables, images, charts
         if shape.has_table:
             table = shape.table
             table_xml = shape._element
 
-            prov = self.generate_prov(shape, slide_ind, "")
+            prov = self.generate_prov(shape, slide_ind, "", slide_size)
 
             num_cols = 0
             num_rows = len(table.rows)
@@ -375,17 +381,19 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
                 name=f"slide-{slide_ind}", label=GroupLabel.CHAPTER, parent=parents[0]
             )
 
-            size = Size(width=slide_width, height=slide_height)
-            parent_page = doc.add_page(page_no=slide_ind + 1, size=size)
+            slide_size = Size(width=slide_width, height=slide_height)
+            parent_page = doc.add_page(page_no=slide_ind + 1, size=slide_size)
 
-            def handle_shapes(shape, parent_slide, slide_ind, doc):
-                handle_groups(shape, parent_slide, slide_ind, doc)
+            def handle_shapes(shape, parent_slide, slide_ind, doc, slide_size):
+                handle_groups(shape, parent_slide, slide_ind, doc, slide_size)
                 if shape.has_table:
                     # Handle Tables
-                    self.handle_tables(shape, parent_slide, slide_ind, doc)
+                    self.handle_tables(shape, parent_slide, slide_ind, doc, slide_size)
                 if shape.shape_type == MSO_SHAPE_TYPE.PICTURE:
                     # Handle Pictures
-                    self.handle_pictures(shape, parent_slide, slide_ind, doc)
+                    self.handle_pictures(
+                        shape, parent_slide, slide_ind, doc, slide_size
+                    )
                 # If shape doesn't have any text, move on to the next shape
                 if not hasattr(shape, "text"):
                     return
@@ -397,16 +405,20 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
                     _log.warning("Warning: shape has text but not text_frame")
                     return
                 # Handle other text elements, including lists (bullet lists, numbered lists)
-                self.handle_text_elements(shape, parent_slide, slide_ind, doc)
+                self.handle_text_elements(
+                    shape, parent_slide, slide_ind, doc, slide_size
+                )
                 return
 
-            def handle_groups(shape, parent_slide, slide_ind, doc):
+            def handle_groups(shape, parent_slide, slide_ind, doc, slide_size):
                 if shape.shape_type == MSO_SHAPE_TYPE.GROUP:
                     for groupedshape in shape.shapes:
-                        handle_shapes(groupedshape, parent_slide, slide_ind, doc)
+                        handle_shapes(
+                            groupedshape, parent_slide, slide_ind, doc, slide_size
+                        )
 
             # Loop through each shape in the slide
             for shape in slide.shapes:
-                handle_shapes(shape, parent_slide, slide_ind, doc)
+                handle_shapes(shape, parent_slide, slide_ind, doc, slide_size)
 
         return doc
diff --git a/docling/backend/msword_backend.py b/docling/backend/msword_backend.py
index f59356e2..4d4026e3 100644
--- a/docling/backend/msword_backend.py
+++ b/docling/backend/msword_backend.py
@@ -2,21 +2,28 @@ import logging
 import re
 from io import BytesIO
 from pathlib import Path
-from typing import Set, Union
+from typing import Any, Optional, Union
 
-import docx
 from docling_core.types.doc import (
     DocItemLabel,
     DoclingDocument,
     DocumentOrigin,
     GroupLabel,
     ImageRef,
+    NodeItem,
     TableCell,
     TableData,
 )
+from docx import Document
+from docx.document import Document as DocxDocument
+from docx.oxml.table import CT_Tc
+from docx.oxml.xmlchemy import BaseOxmlElement
+from docx.table import Table, _Cell
+from docx.text.paragraph import Paragraph
 from lxml import etree
 from lxml.etree import XPath
 from PIL import Image, UnidentifiedImageError
+from typing_extensions import override
 
 from docling.backend.abstract_backend import DeclarativeDocumentBackend
 from docling.datamodel.base_models import InputFormat
@@ -26,8 +33,10 @@ _log = logging.getLogger(__name__)
 
 
 class MsWordDocumentBackend(DeclarativeDocumentBackend):
-
-    def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
+    @override
+    def __init__(
+        self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]
+    ) -> None:
         super().__init__(in_doc, path_or_stream)
         self.XML_KEY = (
             "{http://schemas.openxmlformats.org/wordprocessingml/2006/main}val"
@@ -37,19 +46,19 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         }
         # self.initialise(path_or_stream)
         # Word file:
-        self.path_or_stream = path_or_stream
-        self.valid = False
+        self.path_or_stream: Union[BytesIO, Path] = path_or_stream
+        self.valid: bool = False
         # Initialise the parents for the hierarchy
-        self.max_levels = 10
-        self.level_at_new_list = None
-        self.parents = {}  # type: ignore
+        self.max_levels: int = 10
+        self.level_at_new_list: Optional[int] = None
+        self.parents: dict[int, Optional[NodeItem]] = {}
         for i in range(-1, self.max_levels):
             self.parents[i] = None
 
         self.level = 0
         self.listIter = 0
 
-        self.history = {
+        self.history: dict[str, Any] = {
             "names": [None],
             "levels": [None],
             "numids": [None],
@@ -59,9 +68,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         self.docx_obj = None
         try:
             if isinstance(self.path_or_stream, BytesIO):
-                self.docx_obj = docx.Document(self.path_or_stream)
+                self.docx_obj = Document(self.path_or_stream)
             elif isinstance(self.path_or_stream, Path):
-                self.docx_obj = docx.Document(str(self.path_or_stream))
+                self.docx_obj = Document(str(self.path_or_stream))
 
             self.valid = True
         except Exception as e:
@@ -69,13 +78,16 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 f"MsPowerpointDocumentBackend could not load document with hash {self.document_hash}"
             ) from e
 
+    @override
     def is_valid(self) -> bool:
         return self.valid
 
     @classmethod
+    @override
     def supports_pagination(cls) -> bool:
         return False
 
+    @override
     def unload(self):
         if isinstance(self.path_or_stream, BytesIO):
             self.path_or_stream.close()
@@ -83,11 +95,17 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         self.path_or_stream = None
 
     @classmethod
-    def supported_formats(cls) -> Set[InputFormat]:
+    @override
+    def supported_formats(cls) -> set[InputFormat]:
         return {InputFormat.DOCX}
 
+    @override
     def convert(self) -> DoclingDocument:
-        # Parses the DOCX into a structured document model.
+        """Parses the DOCX into a structured document model.
+
+        Returns:
+            The parsed document.
+        """
 
         origin = DocumentOrigin(
             filename=self.file.name or "file",
@@ -105,23 +123,29 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 f"Cannot convert doc with {self.document_hash} because the backend failed to init."
             )
 
-    def update_history(self, name, level, numid, ilevel):
+    def update_history(
+        self,
+        name: str,
+        level: Optional[int],
+        numid: Optional[int],
+        ilevel: Optional[int],
+    ):
         self.history["names"].append(name)
         self.history["levels"].append(level)
 
         self.history["numids"].append(numid)
         self.history["indents"].append(ilevel)
 
-    def prev_name(self):
+    def prev_name(self) -> Optional[str]:
         return self.history["names"][-1]
 
-    def prev_level(self):
+    def prev_level(self) -> Optional[int]:
         return self.history["levels"][-1]
 
-    def prev_numid(self):
+    def prev_numid(self) -> Optional[int]:
         return self.history["numids"][-1]
 
-    def prev_indent(self):
+    def prev_indent(self) -> Optional[int]:
         return self.history["indents"][-1]
 
     def get_level(self) -> int:
@@ -131,13 +155,19 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 return k
         return 0
 
-    def walk_linear(self, body, docx_obj, doc) -> DoclingDocument:
+    def walk_linear(
+        self,
+        body: BaseOxmlElement,
+        docx_obj: DocxDocument,
+        doc: DoclingDocument,
+    ) -> DoclingDocument:
         for element in body:
             tag_name = etree.QName(element).localname
             # Check for Inline Images (blip elements)
             namespaces = {
                 "a": "http://schemas.openxmlformats.org/drawingml/2006/main",
                 "r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships",
+                "w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main",
             }
             xpath_expr = XPath(".//a:blip", namespaces=namespaces)
             drawing_blip = xpath_expr(element)
@@ -150,7 +180,15 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                     _log.debug("could not parse a table, broken docx table")
 
             elif drawing_blip:
-                self.handle_pictures(element, docx_obj, drawing_blip, doc)
+                self.handle_pictures(docx_obj, drawing_blip, doc)
+            # Check for the sdt containers, like table of contents
+            elif tag_name in ["sdt"]:
+                sdt_content = element.find(".//w:sdtContent", namespaces=namespaces)
+                if sdt_content is not None:
+                    # Iterate paragraphs, runs, or text inside <w:sdtContent>.
+                    paragraphs = sdt_content.findall(".//w:p", namespaces=namespaces)
+                    for p in paragraphs:
+                        self.handle_text_elements(p, docx_obj, doc)
             # Check for Text
             elif tag_name in ["p"]:
                 # "tcPr", "sectPr"
@@ -159,7 +197,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 _log.debug(f"Ignoring element in DOCX with tag: {tag_name}")
         return doc
 
-    def str_to_int(self, s, default=0):
+    def str_to_int(self, s: Optional[str], default: Optional[int] = 0) -> Optional[int]:
         if s is None:
             return None
         try:
@@ -167,7 +205,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         except ValueError:
             return default
 
-    def split_text_and_number(self, input_string):
+    def split_text_and_number(self, input_string: str) -> list[str]:
         match = re.match(r"(\D+)(\d+)$|^(\d+)(\D+)", input_string)
         if match:
             parts = list(filter(None, match.groups()))
@@ -175,7 +213,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         else:
             return [input_string]
 
-    def get_numId_and_ilvl(self, paragraph):
+    def get_numId_and_ilvl(
+        self, paragraph: Paragraph
+    ) -> tuple[Optional[int], Optional[int]]:
         # Access the XML element of the paragraph
         numPr = paragraph._element.find(
             ".//w:numPr", namespaces=paragraph._element.nsmap
@@ -188,13 +228,11 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             numId = numId_elem.get(self.XML_KEY) if numId_elem is not None else None
             ilvl = ilvl_elem.get(self.XML_KEY) if ilvl_elem is not None else None
 
-            return self.str_to_int(numId, default=None), self.str_to_int(
-                ilvl, default=None
-            )
+            return self.str_to_int(numId, None), self.str_to_int(ilvl, None)
 
         return None, None  # If the paragraph is not part of a list
 
-    def get_label_and_level(self, paragraph):
+    def get_label_and_level(self, paragraph: Paragraph) -> tuple[str, Optional[int]]:
         if paragraph.style is None:
             return "Normal", None
         label = paragraph.style.style_id
@@ -210,20 +248,25 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
 
         if "Heading" in label and len(parts) == 2:
             parts.sort()
-            label_str = ""
-            label_level = 0
+            label_str: str = ""
+            label_level: Optional[int] = 0
             if parts[0] == "Heading":
                 label_str = parts[0]
-                label_level = self.str_to_int(parts[1], default=None)
+                label_level = self.str_to_int(parts[1], None)
             if parts[1] == "Heading":
                 label_str = parts[1]
-                label_level = self.str_to_int(parts[0], default=None)
+                label_level = self.str_to_int(parts[0], None)
             return label_str, label_level
         else:
             return label, None
 
-    def handle_text_elements(self, element, docx_obj, doc):
-        paragraph = docx.text.paragraph.Paragraph(element, docx_obj)
+    def handle_text_elements(
+        self,
+        element: BaseOxmlElement,
+        docx_obj: DocxDocument,
+        doc: DoclingDocument,
+    ) -> None:
+        paragraph = Paragraph(element, docx_obj)
 
         if paragraph.text is None:
             return
@@ -241,13 +284,13 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             numid = None
 
         # Handle lists
-        if numid is not None and ilevel is not None:
+        if (
+            numid is not None
+            and ilevel is not None
+            and p_style_id not in ["Title", "Heading"]
+        ):
             self.add_listitem(
-                element,
-                docx_obj,
                 doc,
-                p_style_id,
-                p_level,
                 numid,
                 ilevel,
                 text,
@@ -255,20 +298,30 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             )
             self.update_history(p_style_id, p_level, numid, ilevel)
             return
-        elif numid is None and self.prev_numid() is not None:  # Close list
-            for key, val in self.parents.items():
-                if key >= self.level_at_new_list:
+        elif (
+            numid is None
+            and self.prev_numid() is not None
+            and p_style_id not in ["Title", "Heading"]
+        ):  # Close list
+            if self.level_at_new_list:
+                for key in range(len(self.parents)):
+                    if key >= self.level_at_new_list:
+                        self.parents[key] = None
+                self.level = self.level_at_new_list - 1
+                self.level_at_new_list = None
+            else:
+                for key in range(len(self.parents)):
                     self.parents[key] = None
-            self.level = self.level_at_new_list - 1
-            self.level_at_new_list = None
+                self.level = 0
+
         if p_style_id in ["Title"]:
-            for key, val in self.parents.items():
+            for key in range(len(self.parents)):
                 self.parents[key] = None
             self.parents[0] = doc.add_text(
                 parent=None, label=DocItemLabel.TITLE, text=text
             )
         elif "Heading" in p_style_id:
-            self.add_header(element, docx_obj, doc, p_style_id, p_level, text)
+            self.add_header(doc, p_level, text)
 
         elif p_style_id in [
             "Paragraph",
@@ -296,7 +349,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
         self.update_history(p_style_id, p_level, numid, ilevel)
         return
 
-    def add_header(self, element, docx_obj, doc, curr_name, curr_level, text: str):
+    def add_header(
+        self, doc: DoclingDocument, curr_level: Optional[int], text: str
+    ) -> None:
         level = self.get_level()
         if isinstance(curr_level, int):
             if curr_level > level:
@@ -309,7 +364,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                     )
             elif curr_level < level:
                 # remove the tail
-                for key, val in self.parents.items():
+                for key in range(len(self.parents)):
                     if key >= curr_level:
                         self.parents[key] = None
 
@@ -328,22 +383,18 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
 
     def add_listitem(
         self,
-        element,
-        docx_obj,
-        doc,
-        p_style_id,
-        p_level,
-        numid,
-        ilevel,
+        doc: DoclingDocument,
+        numid: int,
+        ilevel: int,
         text: str,
-        is_numbered=False,
-    ):
-        # is_numbered = is_numbered
+        is_numbered: bool = False,
+    ) -> None:
         enum_marker = ""
 
         level = self.get_level()
+        prev_indent = self.prev_indent()
         if self.prev_numid() is None:  # Open new list
-            self.level_at_new_list = level  # type: ignore
+            self.level_at_new_list = level
 
             self.parents[level] = doc.add_group(
                 label=GroupLabel.LIST, name="list", parent=self.parents[level - 1]
@@ -362,10 +413,13 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             )
 
         elif (
-            self.prev_numid() == numid and self.prev_indent() < ilevel
+            self.prev_numid() == numid
+            and self.level_at_new_list is not None
+            and prev_indent is not None
+            and prev_indent < ilevel
         ):  # Open indented list
             for i in range(
-                self.level_at_new_list + self.prev_indent() + 1,
+                self.level_at_new_list + prev_indent + 1,
                 self.level_at_new_list + ilevel + 1,
             ):
                 # Determine if this is an unordered list or an ordered list.
@@ -394,7 +448,12 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 text=text,
             )
 
-        elif self.prev_numid() == numid and ilevel < self.prev_indent():  # Close list
+        elif (
+            self.prev_numid() == numid
+            and self.level_at_new_list is not None
+            and prev_indent is not None
+            and ilevel < prev_indent
+        ):  # Close list
             for k, v in self.parents.items():
                 if k > self.level_at_new_list + ilevel:
                     self.parents[k] = None
@@ -412,7 +471,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             )
             self.listIter = 0
 
-        elif self.prev_numid() == numid or self.prev_indent() == ilevel:
+        elif self.prev_numid() == numid or prev_indent == ilevel:
             # TODO: Set marker and enumerated arguments if this is an enumeration element.
             self.listIter += 1
             if is_numbered:
@@ -426,31 +485,16 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             )
         return
 
-    def handle_tables(self, element, docx_obj, doc):
-
-        # Function to check if a cell has a colspan (gridSpan)
-        def get_colspan(cell):
-            grid_span = cell._element.xpath("@w:gridSpan")
-            if grid_span:
-                return int(grid_span[0])  # Return the number of columns spanned
-            return 1  # Default is 1 (no colspan)
-
-        # Function to check if a cell has a rowspan (vMerge)
-        def get_rowspan(cell):
-            v_merge = cell._element.xpath("@w:vMerge")
-            if v_merge:
-                return v_merge[
-                    0
-                ]  # 'restart' indicates the beginning of a rowspan, others are continuation
-            return 1
-
-        table = docx.table.Table(element, docx_obj)
-
+    def handle_tables(
+        self,
+        element: BaseOxmlElement,
+        docx_obj: DocxDocument,
+        doc: DoclingDocument,
+    ) -> None:
+        table: Table = Table(element, docx_obj)
         num_rows = len(table.rows)
-        num_cols = 0
-        for row in table.rows:
-            # Calculate the max number of columns
-            num_cols = max(num_cols, sum(get_colspan(cell) for cell in row.cells))
+        num_cols = len(table.columns)
+        _log.debug(f"Table grid with {num_rows} rows and {num_cols} columns")
 
         if num_rows == 1 and num_cols == 1:
             cell_element = table.rows[0].cells[0]
@@ -459,59 +503,56 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
             self.walk_linear(cell_element._element, docx_obj, doc)
             return
 
-        # Initialize the table grid
-        table_grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
-
-        data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
-
+        data = TableData(num_rows=num_rows, num_cols=num_cols)
+        cell_set: set[CT_Tc] = set()
         for row_idx, row in enumerate(table.rows):
+            _log.debug(f"Row index {row_idx} with {len(row.cells)} populated cells")
             col_idx = 0
-            for c, cell in enumerate(row.cells):
-                row_span = get_rowspan(cell)
-                col_span = get_colspan(cell)
+            while col_idx < num_cols:
+                cell: _Cell = row.cells[col_idx]
+                _log.debug(
+                    f" col {col_idx} grid_span {cell.grid_span} grid_cols_before {row.grid_cols_before}"
+                )
+                if cell is None or cell._tc in cell_set:
+                    _log.debug(f"  skipped since repeated content")
+                    col_idx += cell.grid_span
+                    continue
+                else:
+                    cell_set.add(cell._tc)
 
-                cell_text = cell.text
-                # In case cell doesn't return text via docx library:
-                if len(cell_text) == 0:
-                    cell_xml = cell._element
+                spanned_idx = row_idx
+                spanned_tc: Optional[CT_Tc] = cell._tc
+                while spanned_tc == cell._tc:
+                    spanned_idx += 1
+                    spanned_tc = (
+                        table.rows[spanned_idx].cells[col_idx]._tc
+                        if spanned_idx < num_rows
+                        else None
+                    )
+                _log.debug(f"  spanned before row {spanned_idx}")
 
-                    texts = [""]
-                    for elem in cell_xml.iter():
-                        if elem.tag.endswith("t"):  # <w:t> tags that contain text
-                            if elem.text:
-                                texts.append(elem.text)
-                    # Join the collected text
-                    cell_text = " ".join(texts).strip()
-
-                # Find the next available column in the grid
-                while table_grid[row_idx][col_idx] is not None:
-                    col_idx += 1
-
-                # Fill the grid with the cell value, considering rowspan and colspan
-                for i in range(row_span if row_span == "restart" else 1):
-                    for j in range(col_span):
-                        table_grid[row_idx + i][col_idx + j] = ""
-
-                cell = TableCell(
-                    text=cell_text,
-                    row_span=row_span,
-                    col_span=col_span,
-                    start_row_offset_idx=row_idx,
-                    end_row_offset_idx=row_idx + row_span,
+                table_cell = TableCell(
+                    text=cell.text,
+                    row_span=spanned_idx - row_idx,
+                    col_span=cell.grid_span,
+                    start_row_offset_idx=row.grid_cols_before + row_idx,
+                    end_row_offset_idx=row.grid_cols_before + spanned_idx,
                     start_col_offset_idx=col_idx,
-                    end_col_offset_idx=col_idx + col_span,
+                    end_col_offset_idx=col_idx + cell.grid_span,
                     col_header=False,
                     row_header=False,
                 )
-
-                data.table_cells.append(cell)
+                data.table_cells.append(table_cell)
+                col_idx += cell.grid_span
 
         level = self.get_level()
         doc.add_table(data=data, parent=self.parents[level - 1])
         return
 
-    def handle_pictures(self, element, docx_obj, drawing_blip, doc):
-        def get_docx_image(element, drawing_blip):
+    def handle_pictures(
+        self, docx_obj: DocxDocument, drawing_blip: Any, doc: DoclingDocument
+    ) -> None:
+        def get_docx_image(drawing_blip):
             rId = drawing_blip[0].get(
                 "{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed"
             )
@@ -521,11 +562,11 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
                 image_data = image_part.blob  # Get the binary image data
             return image_data
 
-        image_data = get_docx_image(element, drawing_blip)
-        image_bytes = BytesIO(image_data)
         level = self.get_level()
         # Open the BytesIO object with PIL to create an Image
         try:
+            image_data = get_docx_image(drawing_blip)
+            image_bytes = BytesIO(image_data)
             pil_image = Image.open(image_bytes)
             doc.add_picture(
                 parent=self.parents[level - 1],
diff --git a/docling/backend/pdf_backend.py b/docling/backend/pdf_backend.py
index cd7a0815..35c83b8c 100644
--- a/docling/backend/pdf_backend.py
+++ b/docling/backend/pdf_backend.py
@@ -12,7 +12,6 @@ from docling.datamodel.document import InputDocument
 
 
 class PdfPageBackend(ABC):
-
     @abstractmethod
     def get_text_in_rect(self, bbox: BoundingBox) -> str:
         pass
@@ -45,7 +44,6 @@ class PdfPageBackend(ABC):
 
 
 class PdfDocumentBackend(PaginatedDocumentBackend):
-
     def __init__(self, in_doc: InputDocument, path_or_stream: Union[BytesIO, Path]):
         super().__init__(in_doc, path_or_stream)
 
diff --git a/docling/backend/pypdfium2_backend.py b/docling/backend/pypdfium2_backend.py
index 2566fc18..5b627da7 100644
--- a/docling/backend/pypdfium2_backend.py
+++ b/docling/backend/pypdfium2_backend.py
@@ -210,7 +210,7 @@ class PyPdfiumPageBackend(PdfPageBackend):
                 l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT
             )
         else:
-            padbox = cropbox.to_bottom_left_origin(page_size.height)
+            padbox = cropbox.to_bottom_left_origin(page_size.height).model_copy()
             padbox.r = page_size.width - padbox.r
             padbox.t = page_size.height - padbox.t
 
diff --git a/docling/backend/xml/uspto_backend.py b/docling/backend/xml/uspto_backend.py
index ef253b21..21001ab7 100644
--- a/docling/backend/xml/uspto_backend.py
+++ b/docling/backend/xml/uspto_backend.py
@@ -389,7 +389,7 @@ class PatentUsptoIce(PatentUspto):
             if name == self.Element.TITLE.value:
                 if text:
                     self.parents[self.level + 1] = self.doc.add_title(
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                         text=text,
                     )
                     self.level += 1
@@ -406,7 +406,7 @@ class PatentUsptoIce(PatentUspto):
                     abstract_item = self.doc.add_heading(
                         heading_text,
                         level=heading_level,
-                        parent=self.parents[heading_level],  # type: ignore[arg-type]
+                        parent=self.parents[heading_level],
                     )
                     self.doc.add_text(
                         label=DocItemLabel.PARAGRAPH,
@@ -434,7 +434,7 @@ class PatentUsptoIce(PatentUspto):
                 claims_item = self.doc.add_heading(
                     heading_text,
                     level=heading_level,
-                    parent=self.parents[heading_level],  # type: ignore[arg-type]
+                    parent=self.parents[heading_level],
                 )
                 for text in self.claims:
                     self.doc.add_text(
@@ -452,7 +452,7 @@ class PatentUsptoIce(PatentUspto):
                     self.doc.add_text(
                         label=DocItemLabel.PARAGRAPH,
                         text=text,
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                     )
                 self.text = ""
 
@@ -460,7 +460,7 @@ class PatentUsptoIce(PatentUspto):
                 self.parents[self.level + 1] = self.doc.add_heading(
                     text=text,
                     level=self.level,
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
                 self.level += 1
                 self.text = ""
@@ -470,7 +470,7 @@ class PatentUsptoIce(PatentUspto):
                 empty_table = TableData(num_rows=0, num_cols=0, table_cells=[])
                 self.doc.add_table(
                     data=empty_table,
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
 
         def _apply_style(self, text: str, style_tag: str) -> str:
@@ -721,7 +721,7 @@ class PatentUsptoGrantV2(PatentUspto):
                 if self.Element.TITLE.value in self.property and text.strip():
                     title = text.strip()
                     self.parents[self.level + 1] = self.doc.add_title(
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                         text=title,
                     )
                     self.level += 1
@@ -749,7 +749,7 @@ class PatentUsptoGrantV2(PatentUspto):
                     self.parents[self.level + 1] = self.doc.add_heading(
                         text=text.strip(),
                         level=self.level,
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                     )
                     self.level += 1
 
@@ -769,7 +769,7 @@ class PatentUsptoGrantV2(PatentUspto):
                 claims_item = self.doc.add_heading(
                     heading_text,
                     level=heading_level,
-                    parent=self.parents[heading_level],  # type: ignore[arg-type]
+                    parent=self.parents[heading_level],
                 )
                 for text in self.claims:
                     self.doc.add_text(
@@ -787,7 +787,7 @@ class PatentUsptoGrantV2(PatentUspto):
                 abstract_item = self.doc.add_heading(
                     heading_text,
                     level=heading_level,
-                    parent=self.parents[heading_level],  # type: ignore[arg-type]
+                    parent=self.parents[heading_level],
                 )
                 self.doc.add_text(
                     label=DocItemLabel.PARAGRAPH, text=abstract, parent=abstract_item
@@ -799,7 +799,7 @@ class PatentUsptoGrantV2(PatentUspto):
                     self.doc.add_text(
                         label=DocItemLabel.PARAGRAPH,
                         text=paragraph,
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                     )
                 elif self.Element.CLAIM.value in self.property:
                     # we may need a space after a paragraph in claim text
@@ -811,7 +811,7 @@ class PatentUsptoGrantV2(PatentUspto):
                 empty_table = TableData(num_rows=0, num_cols=0, table_cells=[])
                 self.doc.add_table(
                     data=empty_table,
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
 
         def _apply_style(self, text: str, style_tag: str) -> str:
@@ -938,7 +938,7 @@ class PatentUsptoGrantAps(PatentUspto):
         self.parents[self.level + 1] = self.doc.add_heading(
             heading.value,
             level=self.level,
-            parent=self.parents[self.level],  # type: ignore[arg-type]
+            parent=self.parents[self.level],
         )
         self.level += 1
 
@@ -959,7 +959,7 @@ class PatentUsptoGrantAps(PatentUspto):
 
         if field == self.Field.TITLE.value:
             self.parents[self.level + 1] = self.doc.add_title(
-                parent=self.parents[self.level], text=value  # type: ignore[arg-type]
+                parent=self.parents[self.level], text=value
             )
             self.level += 1
 
@@ -971,14 +971,14 @@ class PatentUsptoGrantAps(PatentUspto):
                 self.doc.add_text(
                     label=DocItemLabel.PARAGRAPH,
                     text=value,
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
 
         elif field == self.Field.NUMBER.value and section == self.Section.CLAIMS.value:
             self.doc.add_text(
                 label=DocItemLabel.PARAGRAPH,
                 text="",
-                parent=self.parents[self.level],  # type: ignore[arg-type]
+                parent=self.parents[self.level],
             )
 
         elif (
@@ -996,7 +996,7 @@ class PatentUsptoGrantAps(PatentUspto):
                 last_claim = self.doc.add_text(
                     label=DocItemLabel.PARAGRAPH,
                     text="",
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
 
             last_claim.text += f" {value}" if last_claim.text else value
@@ -1012,7 +1012,7 @@ class PatentUsptoGrantAps(PatentUspto):
             self.parents[self.level + 1] = self.doc.add_heading(
                 value,
                 level=self.level,
-                parent=self.parents[self.level],  # type: ignore[arg-type]
+                parent=self.parents[self.level],
             )
             self.level += 1
 
@@ -1029,7 +1029,7 @@ class PatentUsptoGrantAps(PatentUspto):
             self.doc.add_text(
                 label=DocItemLabel.PARAGRAPH,
                 text=value,
-                parent=self.parents[self.level],  # type: ignore[arg-type]
+                parent=self.parents[self.level],
             )
 
     def parse(self, patent_content: str) -> Optional[DoclingDocument]:
@@ -1283,7 +1283,7 @@ class PatentUsptoAppV1(PatentUspto):
                 title = text.strip()
                 if title:
                     self.parents[self.level + 1] = self.doc.add_text(
-                        parent=self.parents[self.level],  # type: ignore[arg-type]
+                        parent=self.parents[self.level],
                         label=DocItemLabel.TITLE,
                         text=title,
                     )
@@ -1301,7 +1301,7 @@ class PatentUsptoAppV1(PatentUspto):
                     abstract_item = self.doc.add_heading(
                         heading_text,
                         level=heading_level,
-                        parent=self.parents[heading_level],  # type: ignore[arg-type]
+                        parent=self.parents[heading_level],
                     )
                     self.doc.add_text(
                         label=DocItemLabel.PARAGRAPH,
@@ -1331,7 +1331,7 @@ class PatentUsptoAppV1(PatentUspto):
                 claims_item = self.doc.add_heading(
                     heading_text,
                     level=heading_level,
-                    parent=self.parents[heading_level],  # type: ignore[arg-type]
+                    parent=self.parents[heading_level],
                 )
                 for text in self.claims:
                     self.doc.add_text(
@@ -1350,14 +1350,14 @@ class PatentUsptoAppV1(PatentUspto):
                         self.parents[self.level + 1] = self.doc.add_heading(
                             text=text,
                             level=self.level,
-                            parent=self.parents[self.level],  # type: ignore[arg-type]
+                            parent=self.parents[self.level],
                         )
                         self.level += 1
                     else:
                         self.doc.add_text(
                             label=DocItemLabel.PARAGRAPH,
                             text=text,
-                            parent=self.parents[self.level],  # type: ignore[arg-type]
+                            parent=self.parents[self.level],
                         )
                 self.text = ""
 
@@ -1366,7 +1366,7 @@ class PatentUsptoAppV1(PatentUspto):
                 empty_table = TableData(num_rows=0, num_cols=0, table_cells=[])
                 self.doc.add_table(
                     data=empty_table,
-                    parent=self.parents[self.level],  # type: ignore[arg-type]
+                    parent=self.parents[self.level],
                 )
 
         def _apply_style(self, text: str, style_tag: str) -> str:
diff --git a/docling/cli/main.py b/docling/cli/main.py
index e1ce289e..7d31221d 100644
--- a/docling/cli/main.py
+++ b/docling/cli/main.py
@@ -1,18 +1,18 @@
 import importlib
-import json
 import logging
+import platform
 import re
+import sys
 import tempfile
 import time
 import warnings
-from enum import Enum
 from pathlib import Path
 from typing import Annotated, Dict, Iterable, List, Optional, Type
 
 import typer
 from docling_core.types.doc import ImageRefMode
 from docling_core.utils.file import resolve_source_to_path
-from pydantic import TypeAdapter, ValidationError
+from pydantic import TypeAdapter
 
 from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
 from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
@@ -65,10 +65,15 @@ def version_callback(value: bool):
         docling_core_version = importlib.metadata.version("docling-core")
         docling_ibm_models_version = importlib.metadata.version("docling-ibm-models")
         docling_parse_version = importlib.metadata.version("docling-parse")
+        platform_str = platform.platform()
+        py_impl_version = sys.implementation.cache_tag
+        py_lang_version = platform.python_version()
         print(f"Docling version: {docling_version}")
         print(f"Docling Core version: {docling_core_version}")
         print(f"Docling IBM Models version: {docling_ibm_models_version}")
         print(f"Docling Parse version: {docling_parse_version}")
+        print(f"Python: {py_impl_version} ({py_lang_version})")
+        print(f"Platform: {platform_str}")
         raise typer.Exit()
 
 
@@ -206,6 +211,14 @@ def convert(
         TableFormerMode,
         typer.Option(..., help="The mode to use in the table structure model."),
     ] = TableFormerMode.FAST,
+    enrich_code: Annotated[
+        bool,
+        typer.Option(..., help="Enable the code enrichment model in the pipeline."),
+    ] = False,
+    enrich_formula: Annotated[
+        bool,
+        typer.Option(..., help="Enable the formula enrichment model in the pipeline."),
+    ] = False,
     artifacts_path: Annotated[
         Optional[Path],
         typer.Option(..., help="If provided, the location of the model artifacts."),
@@ -360,6 +373,8 @@ def convert(
             do_ocr=ocr,
             ocr_options=ocr_options,
             do_table_structure=True,
+            do_code_enrichment=enrich_code,
+            do_formula_enrichment=enrich_formula,
             document_timeout=document_timeout,
         )
         pipeline_options.table_structure_options.do_cell_matching = (
diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py
index 5bd28ed6..d1e7ce3a 100644
--- a/docling/datamodel/base_models.py
+++ b/docling/datamodel/base_models.py
@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Union
 from docling_core.types.doc import (
     BoundingBox,
     DocItemLabel,
+    NodeItem,
     PictureDataType,
     Size,
     TableCell,
@@ -40,6 +41,7 @@ class InputFormat(str, Enum):
     MD = "md"
     XLSX = "xlsx"
     XML_USPTO = "xml_uspto"
+    JSON_DOCLING = "json_docling"
 
 
 class OutputFormat(str, Enum):
@@ -61,6 +63,7 @@ FormatToExtensions: Dict[InputFormat, List[str]] = {
     InputFormat.ASCIIDOC: ["adoc", "asciidoc", "asc"],
     InputFormat.XLSX: ["xlsx"],
     InputFormat.XML_USPTO: ["xml", "txt"],
+    InputFormat.JSON_DOCLING: ["json"],
 }
 
 FormatToMimeType: Dict[InputFormat, List[str]] = {
@@ -89,6 +92,7 @@ FormatToMimeType: Dict[InputFormat, List[str]] = {
         "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
     ],
     InputFormat.XML_USPTO: ["application/xml", "text/plain"],
+    InputFormat.JSON_DOCLING: ["application/json"],
 }
 
 MimeTypeToFormat: dict[str, list[InputFormat]] = {
@@ -201,6 +205,13 @@ class AssembledUnit(BaseModel):
     headers: List[PageElement] = []
 
 
+class ItemAndImageEnrichmentElement(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    item: NodeItem
+    image: Image
+
+
 class Page(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -219,12 +230,28 @@ class Page(BaseModel):
         {}
     )  # Cache of images in different scales. By default it is cleared during assembling.
 
-    def get_image(self, scale: float = 1.0) -> Optional[Image]:
+    def get_image(
+        self, scale: float = 1.0, cropbox: Optional[BoundingBox] = None
+    ) -> Optional[Image]:
         if self._backend is None:
             return self._image_cache.get(scale, None)
+
         if not scale in self._image_cache:
-            self._image_cache[scale] = self._backend.get_page_image(scale=scale)
-        return self._image_cache[scale]
+            if cropbox is None:
+                self._image_cache[scale] = self._backend.get_page_image(scale=scale)
+            else:
+                return self._backend.get_page_image(scale=scale, cropbox=cropbox)
+
+        if cropbox is None:
+            return self._image_cache[scale]
+        else:
+            page_im = self._image_cache[scale]
+            assert self.size is not None
+            return page_im.crop(
+                cropbox.to_top_left_origin(page_height=self.size.height)
+                .scaled(scale=scale)
+                .as_tuple()
+            )
 
     @property
     def image(self) -> Optional[Image]:
diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py
index 136428e8..d887fed9 100644
--- a/docling/datamodel/document.py
+++ b/docling/datamodel/document.py
@@ -157,6 +157,8 @@ class InputDocument(BaseModel):
                     self.page_count = self._backend.page_count()
                     if not self.page_count <= self.limits.max_num_pages:
                         self.valid = False
+                    elif self.page_count < self.limits.page_range[0]:
+                        self.valid = False
 
         except (FileNotFoundError, OSError) as e:
             self.valid = False
@@ -350,6 +352,10 @@ class _DocumentConversionInput(BaseModel):
             mime = FormatToMimeType[InputFormat.HTML][0]
         elif ext in FormatToExtensions[InputFormat.MD]:
             mime = FormatToMimeType[InputFormat.MD][0]
+        elif ext in FormatToExtensions[InputFormat.JSON_DOCLING]:
+            mime = FormatToMimeType[InputFormat.JSON_DOCLING][0]
+        elif ext in FormatToExtensions[InputFormat.PDF]:
+            mime = FormatToMimeType[InputFormat.PDF][0]
         return mime
 
     @staticmethod
diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py
index eeec6bab..14ca75bf 100644
--- a/docling/datamodel/pipeline_options.py
+++ b/docling/datamodel/pipeline_options.py
@@ -1,17 +1,11 @@
 import logging
 import os
-import warnings
 from enum import Enum
 from pathlib import Path
-from typing import Annotated, Any, Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Any, List, Literal, Optional, Union
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
-from pydantic_settings import (
-    BaseSettings,
-    PydanticBaseSettingsSource,
-    SettingsConfigDict,
-)
-from typing_extensions import deprecated
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
 
 _log = logging.getLogger(__name__)
 
@@ -125,6 +119,7 @@ class RapidOcrOptions(OcrOptions):
     det_model_path: Optional[str] = None  # same default as rapidocr
     cls_model_path: Optional[str] = None  # same default as rapidocr
     rec_model_path: Optional[str] = None  # same default as rapidocr
+    rec_keys_path: Optional[str] = None  # same default as rapidocr
 
     model_config = ConfigDict(
         extra="forbid",
@@ -225,6 +220,9 @@ class PdfPipelineOptions(PipelineOptions):
     artifacts_path: Optional[Union[Path, str]] = None
     do_table_structure: bool = True  # True: perform table structure extraction
     do_ocr: bool = True  # True: perform OCR, replace programmatic PDF text
+    do_code_enrichment: bool = False  # True: perform code OCR
+    do_formula_enrichment: bool = False  # True: perform formula OCR, return Latex code
+    do_picture_classification: bool = False  # True: classify pictures in documents
 
     table_structure_options: TableStructureOptions = TableStructureOptions()
     ocr_options: Union[
diff --git a/docling/datamodel/settings.py b/docling/datamodel/settings.py
index 46bab75c..92856203 100644
--- a/docling/datamodel/settings.py
+++ b/docling/datamodel/settings.py
@@ -1,13 +1,28 @@
 import sys
 from pathlib import Path
+from typing import Annotated, Tuple
 
-from pydantic import BaseModel
+from pydantic import BaseModel, PlainValidator
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
+def _validate_page_range(v: Tuple[int, int]) -> Tuple[int, int]:
+    if v[0] < 1 or v[1] < v[0]:
+        raise ValueError(
+            "Invalid page range: start must be ≥ 1 and end must be ≥ start."
+        )
+    return v
+
+
+PageRange = Annotated[Tuple[int, int], PlainValidator(_validate_page_range)]
+
+DEFAULT_PAGE_RANGE: PageRange = (1, sys.maxsize)
+
+
 class DocumentLimits(BaseModel):
     max_num_pages: int = sys.maxsize
     max_file_size: int = sys.maxsize
+    page_range: PageRange = DEFAULT_PAGE_RANGE
 
 
 class BatchConcurrencySettings(BaseModel):
diff --git a/docling/document_converter.py b/docling/document_converter.py
index cb073949..d885dd20 100644
--- a/docling/document_converter.py
+++ b/docling/document_converter.py
@@ -1,9 +1,10 @@
 import logging
+import math
 import sys
 import time
 from functools import partial
 from pathlib import Path
-from typing import Dict, Iterable, Iterator, List, Optional, Type, Union
+from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Type, Union
 
 from pydantic import BaseModel, ConfigDict, model_validator, validate_call
 
@@ -11,6 +12,7 @@ from docling.backend.abstract_backend import AbstractDocumentBackend
 from docling.backend.asciidoc_backend import AsciiDocBackend
 from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
 from docling.backend.html_backend import HTMLDocumentBackend
+from docling.backend.json.docling_json_backend import DoclingJSONBackend
 from docling.backend.md_backend import MarkdownDocumentBackend
 from docling.backend.msexcel_backend import MsExcelDocumentBackend
 from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
@@ -30,7 +32,12 @@ from docling.datamodel.document import (
     _DocumentConversionInput,
 )
 from docling.datamodel.pipeline_options import PipelineOptions
-from docling.datamodel.settings import DocumentLimits, settings
+from docling.datamodel.settings import (
+    DEFAULT_PAGE_RANGE,
+    DocumentLimits,
+    PageRange,
+    settings,
+)
 from docling.exceptions import ConversionError
 from docling.pipeline.base_pipeline import BasePipeline
 from docling.pipeline.simple_pipeline import SimplePipeline
@@ -136,6 +143,9 @@ def _get_default_option(format: InputFormat) -> FormatOption:
         InputFormat.PDF: FormatOption(
             pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend
         ),
+        InputFormat.JSON_DOCLING: FormatOption(
+            pipeline_cls=SimplePipeline, backend=DoclingJSONBackend
+        ),
     }
     if (options := format_to_default_options.get(format)) is not None:
         return options
@@ -180,6 +190,7 @@ class DocumentConverter:
         raises_on_error: bool = True,
         max_num_pages: int = sys.maxsize,
         max_file_size: int = sys.maxsize,
+        page_range: PageRange = DEFAULT_PAGE_RANGE,
     ) -> ConversionResult:
         all_res = self.convert_all(
             source=[source],
@@ -187,6 +198,7 @@ class DocumentConverter:
             max_num_pages=max_num_pages,
             max_file_size=max_file_size,
             headers=headers,
+            page_range=page_range,
         )
         return next(all_res)
 
@@ -198,10 +210,12 @@ class DocumentConverter:
         raises_on_error: bool = True,  # True: raises on first conversion error; False: does not raise on conv error
         max_num_pages: int = sys.maxsize,
         max_file_size: int = sys.maxsize,
+        page_range: PageRange = DEFAULT_PAGE_RANGE,
     ) -> Iterator[ConversionResult]:
         limits = DocumentLimits(
             max_num_pages=max_num_pages,
             max_file_size=max_file_size,
+            page_range=page_range,
         )
         conv_input = _DocumentConversionInput(
             path_or_stream_iterator=source, limits=limits, headers=headers
diff --git a/docling/models/base_model.py b/docling/models/base_model.py
index 1147896c..08d728cc 100644
--- a/docling/models/base_model.py
+++ b/docling/models/base_model.py
@@ -1,9 +1,10 @@
 from abc import ABC, abstractmethod
-from typing import Any, Iterable
+from typing import Any, Generic, Iterable, Optional
 
-from docling_core.types.doc import DoclingDocument, NodeItem
+from docling_core.types.doc import BoundingBox, DoclingDocument, NodeItem, TextItem
+from typing_extensions import TypeVar
 
-from docling.datamodel.base_models import Page
+from docling.datamodel.base_models import ItemAndImageEnrichmentElement, Page
 from docling.datamodel.document import ConversionResult
 
 
@@ -15,14 +16,69 @@ class BasePageModel(ABC):
         pass
 
 
-class BaseEnrichmentModel(ABC):
+EnrichElementT = TypeVar("EnrichElementT", default=NodeItem)
+
+
+class GenericEnrichmentModel(ABC, Generic[EnrichElementT]):
 
     @abstractmethod
     def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
         pass
 
     @abstractmethod
-    def __call__(
-        self, doc: DoclingDocument, element_batch: Iterable[NodeItem]
-    ) -> Iterable[Any]:
+    def prepare_element(
+        self, conv_res: ConversionResult, element: NodeItem
+    ) -> Optional[EnrichElementT]:
         pass
+
+    @abstractmethod
+    def __call__(
+        self, doc: DoclingDocument, element_batch: Iterable[EnrichElementT]
+    ) -> Iterable[NodeItem]:
+        pass
+
+
+class BaseEnrichmentModel(GenericEnrichmentModel[NodeItem]):
+
+    def prepare_element(
+        self, conv_res: ConversionResult, element: NodeItem
+    ) -> Optional[NodeItem]:
+        if self.is_processable(doc=conv_res.document, element=element):
+            return element
+        return None
+
+
+class BaseItemAndImageEnrichmentModel(
+    GenericEnrichmentModel[ItemAndImageEnrichmentElement]
+):
+
+    images_scale: float
+    expansion_factor: float = 0.0
+
+    def prepare_element(
+        self, conv_res: ConversionResult, element: NodeItem
+    ) -> Optional[ItemAndImageEnrichmentElement]:
+        if not self.is_processable(doc=conv_res.document, element=element):
+            return None
+
+        assert isinstance(element, TextItem)
+        element_prov = element.prov[0]
+
+        bbox = element_prov.bbox
+        width = bbox.r - bbox.l
+        height = bbox.t - bbox.b
+
+        # TODO: move to a utility in the BoundingBox class
+        expanded_bbox = BoundingBox(
+            l=bbox.l - width * self.expansion_factor,
+            t=bbox.t + height * self.expansion_factor,
+            r=bbox.r + width * self.expansion_factor,
+            b=bbox.b - height * self.expansion_factor,
+            coord_origin=bbox.coord_origin,
+        )
+
+        page_ix = element_prov.page_no - 1
+        cropped_image = conv_res.pages[page_ix].get_image(
+            scale=self.images_scale, cropbox=expanded_bbox
+        )
+        return ItemAndImageEnrichmentElement(item=element, image=cropped_image)
diff --git a/docling/models/code_formula_model.py b/docling/models/code_formula_model.py
new file mode 100644
index 00000000..e4d56945
--- /dev/null
+++ b/docling/models/code_formula_model.py
@@ -0,0 +1,245 @@
+import re
+from pathlib import Path
+from typing import Iterable, List, Literal, Optional, Tuple, Union
+
+from docling_core.types.doc import (
+    CodeItem,
+    DocItemLabel,
+    DoclingDocument,
+    NodeItem,
+    TextItem,
+)
+from docling_core.types.doc.labels import CodeLanguageLabel
+from PIL import Image
+from pydantic import BaseModel
+
+from docling.datamodel.base_models import ItemAndImageEnrichmentElement
+from docling.datamodel.pipeline_options import AcceleratorOptions
+from docling.models.base_model import BaseItemAndImageEnrichmentModel
+from docling.utils.accelerator_utils import decide_device
+
+
+class CodeFormulaModelOptions(BaseModel):
+    """
+    Configuration options for the CodeFormulaModel.
+
+    Attributes
+    ----------
+    kind : str
+        Type of the model. Fixed value "code_formula".
+    do_code_enrichment : bool
+        True if code enrichment is enabled, False otherwise.
+    do_formula_enrichment : bool
+        True if formula enrichment is enabled, False otherwise.
+    """
+
+    kind: Literal["code_formula"] = "code_formula"
+    do_code_enrichment: bool = True
+    do_formula_enrichment: bool = True
+
+
+class CodeFormulaModel(BaseItemAndImageEnrichmentModel):
+    """
+    Model for processing and enriching documents with code and formula predictions.
+
+    Attributes
+    ----------
+    enabled : bool
+        True if the model is enabled, False otherwise.
+    options : CodeFormulaModelOptions
+        Configuration options for the CodeFormulaModel.
+    code_formula_model : CodeFormulaPredictor
+        The predictor model for code and formula processing.
+
+    Methods
+    -------
+    __init__(self, enabled, artifacts_path, accelerator_options, code_formula_options)
+        Initializes the CodeFormulaModel with the given configuration options.
+    is_processable(self, doc, element)
+        Determines if a given element in a document can be processed by the model.
+    __call__(self, doc, element_batch)
+        Processes the given batch of elements and enriches them with predictions.
+    """
+
+    images_scale = 1.66  # = 120 dpi, aligned with training data resolution
+    expansion_factor = 0.03
+
+    def __init__(
+        self,
+        enabled: bool,
+        artifacts_path: Optional[Union[Path, str]],
+        options: CodeFormulaModelOptions,
+        accelerator_options: AcceleratorOptions,
+    ):
+        """
+        Initializes the CodeFormulaModel with the given configuration.
+
+        Parameters
+        ----------
+        enabled : bool
+            True if the model is enabled, False otherwise.
+        artifacts_path : Path
+            Path to the directory containing the model artifacts.
+        options : CodeFormulaModelOptions
+            Configuration options for the model.
+        accelerator_options : AcceleratorOptions
+            Options specifying the device and number of threads for acceleration.
+        """
+        self.enabled = enabled
+        self.options = options
+
+        if self.enabled:
+            device = decide_device(accelerator_options.device)
+
+            from docling_ibm_models.code_formula_model.code_formula_predictor import (
+                CodeFormulaPredictor,
+            )
+
+            if artifacts_path is None:
+                artifacts_path = self.download_models_hf()
+            else:
+                artifacts_path = Path(artifacts_path)
+
+            self.code_formula_model = CodeFormulaPredictor(
+                artifacts_path=artifacts_path,
+                device=device,
+                num_threads=accelerator_options.num_threads,
+            )
+
+    @staticmethod
+    def download_models_hf(
+        local_dir: Optional[Path] = None, force: bool = False
+    ) -> Path:
+        from huggingface_hub import snapshot_download
+        from huggingface_hub.utils import disable_progress_bars
+
+        disable_progress_bars()
+        download_path = snapshot_download(
+            repo_id="ds4sd/CodeFormula",
+            force_download=force,
+            local_dir=local_dir,
+            revision="v1.0.0",
+        )
+
+        return Path(download_path)
+
+    def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
+        """
+        Determines if a given element in a document can be processed by the model.
+
+        Parameters
+        ----------
+        doc : DoclingDocument
+            The document being processed.
+        element : NodeItem
+            The element within the document to check.
+
+        Returns
+        -------
+        bool
+            True if the element can be processed, False otherwise.
+        """
+        return self.enabled and (
+            (isinstance(element, CodeItem) and self.options.do_code_enrichment)
+            or (
+                isinstance(element, TextItem)
+                and element.label == DocItemLabel.FORMULA
+                and self.options.do_formula_enrichment
+            )
+        )
+
+    def _extract_code_language(self, input_string: str) -> Tuple[str, Optional[str]]:
+        """Extracts a programming language from the beginning of a string.
+
+        This function checks if the input string starts with a pattern of the form
+        ``<_some_language_>``. If it does, it extracts the language string and returns
+        a tuple of (remainder, language). Otherwise, it returns the original string
+        and `None`.
+
+        Args:
+            input_string (str): The input string, which may start with ``<_language_>``.
+
+        Returns:
+            Tuple[str, Optional[str]]:
+                A tuple where:
+                - The first element is either:
+                    - The remainder of the string (everything after ``<_language_>``),
+                    if a match is found; or
+                    - The original string, if no match is found.
+                - The second element is the extracted language if a match is found;
+                otherwise, `None`.
+        """
+        pattern = r"^<_([^>]+)_>\s*(.*)"
+        match = re.match(pattern, input_string, flags=re.DOTALL)
+        if match:
+            language = str(match.group(1))  # the captured programming language
+            remainder = str(match.group(2))  # everything after the <_language_>
+            return remainder, language
+        else:
+            return input_string, None
+
+    def _get_code_language_enum(self, value: Optional[str]) -> CodeLanguageLabel:
+        """
+        Converts a string to a corresponding `CodeLanguageLabel` enum member.
+
+        If the provided string does not match any value in `CodeLanguageLabel`,
+        it defaults to `CodeLanguageLabel.UNKNOWN`.
+
+        Args:
+            value (Optional[str]): The string representation of the code language or None.
+
+        Returns:
+            CodeLanguageLabel: The corresponding enum member if the value is valid,
+            otherwise `CodeLanguageLabel.UNKNOWN`.
+        """
+        if not isinstance(value, str):
+            return CodeLanguageLabel.UNKNOWN
+
+        try:
+            return CodeLanguageLabel(value)
+        except ValueError:
+            return CodeLanguageLabel.UNKNOWN
+
+    def __call__(
+        self,
+        doc: DoclingDocument,
+        element_batch: Iterable[ItemAndImageEnrichmentElement],
+    ) -> Iterable[NodeItem]:
+        """
+        Processes the given batch of elements and enriches them with predictions.
+
+        Parameters
+        ----------
+        doc : DoclingDocument
+            The document being processed.
+        element_batch : Iterable[ItemAndImageEnrichmentElement]
+            A batch of elements to be processed.
+
+        Returns
+        -------
+        Iterable[Any]
+            An iterable of enriched elements.
+        """
+        if not self.enabled:
+            for element in element_batch:
+                yield element.item
+            return
+
+        labels: List[str] = []
+        images: List[Image.Image] = []
+        elements: List[TextItem] = []
+        for el in element_batch:
+            assert isinstance(el.item, TextItem)
+            elements.append(el.item)
+            labels.append(el.item.label)
+            images.append(el.image)
+
+        outputs = self.code_formula_model.predict(images, labels)
+
+        for item, output in zip(elements, outputs):
+            if isinstance(item, CodeItem):
+                output, code_language = self._extract_code_language(output)
+                item.code_language = self._get_code_language_enum(code_language)
+            item.text = output
+
+            yield item
diff --git a/docling/models/document_picture_classifier.py b/docling/models/document_picture_classifier.py
new file mode 100644
index 00000000..6e2d90b4
--- /dev/null
+++ b/docling/models/document_picture_classifier.py
@@ -0,0 +1,187 @@
+from pathlib import Path
+from typing import Iterable, List, Literal, Optional, Tuple, Union
+
+from docling_core.types.doc import (
+    DoclingDocument,
+    NodeItem,
+    PictureClassificationClass,
+    PictureClassificationData,
+    PictureItem,
+)
+from PIL import Image
+from pydantic import BaseModel
+
+from docling.datamodel.pipeline_options import AcceleratorOptions
+from docling.models.base_model import BaseEnrichmentModel
+from docling.utils.accelerator_utils import decide_device
+
+
+class DocumentPictureClassifierOptions(BaseModel):
+    """
+    Options for configuring the DocumentPictureClassifier.
+
+    Attributes
+    ----------
+    kind : Literal["document_picture_classifier"]
+        Identifier for the type of classifier.
+    """
+
+    kind: Literal["document_picture_classifier"] = "document_picture_classifier"
+
+
+class DocumentPictureClassifier(BaseEnrichmentModel):
+    """
+    A model for classifying pictures in documents.
+
+    This class enriches document pictures with predicted classifications
+    based on a predefined set of classes.
+
+    Attributes
+    ----------
+    enabled : bool
+        Whether the classifier is enabled for use.
+    options : DocumentPictureClassifierOptions
+        Configuration options for the classifier.
+    document_picture_classifier : DocumentPictureClassifierPredictor
+        The underlying prediction model, loaded if the classifier is enabled.
+
+    Methods
+    -------
+    __init__(enabled, artifacts_path, options, accelerator_options)
+        Initializes the classifier with specified configurations.
+    is_processable(doc, element)
+        Checks if the given element can be processed by the classifier.
+    __call__(doc, element_batch)
+        Processes a batch of elements and adds classification annotations.
+    """
+
+    images_scale = 2
+
+    def __init__(
+        self,
+        enabled: bool,
+        artifacts_path: Optional[Union[Path, str]],
+        options: DocumentPictureClassifierOptions,
+        accelerator_options: AcceleratorOptions,
+    ):
+        """
+        Initializes the DocumentPictureClassifier.
+
+        Parameters
+        ----------
+        enabled : bool
+            Indicates whether the classifier is enabled.
+        artifacts_path : Optional[Union[Path, str]],
+            Path to the directory containing model artifacts.
+        options : DocumentPictureClassifierOptions
+            Configuration options for the classifier.
+        accelerator_options : AcceleratorOptions
+            Options for configuring the device and parallelism.
+        """
+        self.enabled = enabled
+        self.options = options
+
+        if self.enabled:
+            device = decide_device(accelerator_options.device)
+            from docling_ibm_models.document_figure_classifier_model.document_figure_classifier_predictor import (
+                DocumentFigureClassifierPredictor,
+            )
+
+            if artifacts_path is None:
+                artifacts_path = self.download_models_hf()
+            else:
+                artifacts_path = Path(artifacts_path)
+
+            self.document_picture_classifier = DocumentFigureClassifierPredictor(
+                artifacts_path=artifacts_path,
+                device=device,
+                num_threads=accelerator_options.num_threads,
+            )
+
+    @staticmethod
+    def download_models_hf(
+        local_dir: Optional[Path] = None, force: bool = False
+    ) -> Path:
+        from huggingface_hub import snapshot_download
+        from huggingface_hub.utils import disable_progress_bars
+
+        disable_progress_bars()
+        download_path = snapshot_download(
+            repo_id="ds4sd/DocumentFigureClassifier",
+            force_download=force,
+            local_dir=local_dir,
+            revision="v1.0.0",
+        )
+
+        return Path(download_path)
+
+    def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
+        """
+        Determines if the given element can be processed by the classifier.
+
+        Parameters
+        ----------
+        doc : DoclingDocument
+            The document containing the element.
+        element : NodeItem
+            The element to be checked.
+
+        Returns
+        -------
+        bool
+            True if the element is a PictureItem and processing is enabled; False otherwise.
+        """
+        return self.enabled and isinstance(element, PictureItem)
+
+    def __call__(
+        self,
+        doc: DoclingDocument,
+        element_batch: Iterable[NodeItem],
+    ) -> Iterable[NodeItem]:
+        """
+        Processes a batch of elements and enriches them with classification predictions.
+
+        Parameters
+        ----------
+        doc : DoclingDocument
+            The document containing the elements to be processed.
+        element_batch : Iterable[NodeItem]
+            A batch of pictures to classify.
+
+        Returns
+        -------
+        Iterable[NodeItem]
+            An iterable of NodeItem objects after processing. The field
+            'data.classification' is added containing the classification for each picture.
+        """
+        if not self.enabled:
+            for element in element_batch:
+                yield element
+            return
+
+        images: List[Image.Image] = []
+        elements: List[PictureItem] = []
+        for el in element_batch:
+            assert isinstance(el, PictureItem)
+            elements.append(el)
+            img = el.get_image(doc)
+            assert img is not None
+            images.append(img)
+
+        outputs = self.document_picture_classifier.predict(images)
+
+        for element, output in zip(elements, outputs):
+            element.annotations.append(
+                PictureClassificationData(
+                    provenance="DocumentPictureClassifier",
+                    predicted_classes=[
+                        PictureClassificationClass(
+                            class_name=pred[0],
+                            confidence=pred[1],
+                        )
+                        for pred in output
+                    ],
+                )
+            )
+
+            yield element
diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py
index c1b7dabe..69193c94 100644
--- a/docling/models/layout_model.py
+++ b/docling/models/layout_model.py
@@ -1,28 +1,21 @@
 import copy
 import logging
-import random
-import time
 from pathlib import Path
-from typing import Iterable, List
+from typing import Iterable
 
-from docling_core.types.doc import CoordOrigin, DocItemLabel
+from docling_core.types.doc import DocItemLabel
 from docling_ibm_models.layoutmodel.layout_predictor import LayoutPredictor
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image
 
-from docling.datamodel.base_models import (
-    BoundingBox,
-    Cell,
-    Cluster,
-    LayoutPrediction,
-    Page,
-)
+from docling.datamodel.base_models import BoundingBox, Cluster, LayoutPrediction, Page
 from docling.datamodel.document import ConversionResult
-from docling.datamodel.pipeline_options import AcceleratorDevice, AcceleratorOptions
+from docling.datamodel.pipeline_options import AcceleratorOptions
 from docling.datamodel.settings import settings
 from docling.models.base_model import BasePageModel
 from docling.utils.accelerator_utils import decide_device
 from docling.utils.layout_postprocessor import LayoutPostprocessor
 from docling.utils.profiling import TimeRecorder
+from docling.utils.visualization import draw_clusters
 
 _log = logging.getLogger(__name__)
 
@@ -40,7 +33,7 @@ class LayoutModel(BasePageModel):
         DocItemLabel.PAGE_FOOTER,
         DocItemLabel.CODE,
         DocItemLabel.LIST_ITEM,
-        # "Formula",
+        DocItemLabel.FORMULA,
     ]
     PAGE_HEADER_LABELS = [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]
 
@@ -82,78 +75,9 @@ class LayoutModel(BasePageModel):
         left_image = copy.deepcopy(page.image)
         right_image = copy.deepcopy(page.image)
 
-        # Function to draw clusters on an image
-        def draw_clusters(image, clusters):
-            draw = ImageDraw.Draw(image, "RGBA")
-            # Create a smaller font for the labels
-            try:
-                font = ImageFont.truetype("arial.ttf", 12)
-            except OSError:
-                # Fallback to default font if arial is not available
-                font = ImageFont.load_default()
-            for c_tl in clusters:
-                all_clusters = [c_tl, *c_tl.children]
-                for c in all_clusters:
-                    # Draw cells first (underneath)
-                    cell_color = (0, 0, 0, 40)  # Transparent black for cells
-                    for tc in c.cells:
-                        cx0, cy0, cx1, cy1 = tc.bbox.as_tuple()
-                        cx0 *= scale_x
-                        cx1 *= scale_x
-                        cy0 *= scale_x
-                        cy1 *= scale_y
-
-                        draw.rectangle(
-                            [(cx0, cy0), (cx1, cy1)],
-                            outline=None,
-                            fill=cell_color,
-                        )
-                    # Draw cluster rectangle
-                    x0, y0, x1, y1 = c.bbox.as_tuple()
-                    x0 *= scale_x
-                    x1 *= scale_x
-                    y0 *= scale_x
-                    y1 *= scale_y
-
-                    cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70)
-                    cluster_outline_color = (
-                        *list(DocItemLabel.get_color(c.label)),
-                        255,
-                    )
-                    draw.rectangle(
-                        [(x0, y0), (x1, y1)],
-                        outline=cluster_outline_color,
-                        fill=cluster_fill_color,
-                    )
-                    # Add label name and confidence
-                    label_text = f"{c.label.name} ({c.confidence:.2f})"
-                    # Create semi-transparent background for text
-                    text_bbox = draw.textbbox((x0, y0), label_text, font=font)
-                    text_bg_padding = 2
-                    draw.rectangle(
-                        [
-                            (
-                                text_bbox[0] - text_bg_padding,
-                                text_bbox[1] - text_bg_padding,
-                            ),
-                            (
-                                text_bbox[2] + text_bg_padding,
-                                text_bbox[3] + text_bg_padding,
-                            ),
-                        ],
-                        fill=(255, 255, 255, 180),  # Semi-transparent white
-                    )
-                    # Draw text
-                    draw.text(
-                        (x0, y0),
-                        label_text,
-                        fill=(0, 0, 0, 255),  # Solid black
-                        font=font,
-                    )
-
         # Draw clusters on both images
-        draw_clusters(left_image, left_clusters)
-        draw_clusters(right_image, right_clusters)
+        draw_clusters(left_image, left_clusters, scale_x, scale_y)
+        draw_clusters(right_image, right_clusters, scale_x, scale_y)
         # Combine the images side by side
         combined_width = left_image.width * 2
         combined_height = left_image.height
diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py
index 3e202e20..4acf8c95 100644
--- a/docling/models/page_assemble_model.py
+++ b/docling/models/page_assemble_model.py
@@ -22,7 +22,7 @@ _log = logging.getLogger(__name__)
 
 
 class PageAssembleOptions(BaseModel):
-    keep_images: bool = False
+    pass
 
 
 class PageAssembleModel(BasePageModel):
@@ -135,31 +135,6 @@ class PageAssembleModel(BasePageModel):
                                 )
                             elements.append(fig)
                             body.append(fig)
-                        elif cluster.label == LayoutModel.FORMULA_LABEL:
-                            equation = None
-                            if page.predictions.equations_prediction:
-                                equation = page.predictions.equations_prediction.equation_map.get(
-                                    cluster.id, None
-                                )
-                            if (
-                                not equation
-                            ):  # fallback: add empty formula, if it isn't present
-                                text = self.sanitize_text(
-                                    [
-                                        cell.text.replace("\x02", "-").strip()
-                                        for cell in cluster.cells
-                                        if len(cell.text.strip()) > 0
-                                    ]
-                                )
-                                equation = TextElement(
-                                    label=cluster.label,
-                                    id=cluster.id,
-                                    cluster=cluster,
-                                    page_no=page.page_no,
-                                    text=text,
-                                )
-                            elements.append(equation)
-                            body.append(equation)
                         elif cluster.label in LayoutModel.CONTAINER_LABELS:
                             container_el = ContainerElement(
                                 label=cluster.label,
@@ -174,11 +149,4 @@ class PageAssembleModel(BasePageModel):
                         elements=elements, headers=headers, body=body
                     )
 
-                    # Remove page images (can be disabled)
-                    if not self.options.keep_images:
-                        page._image_cache = {}
-
-                    # Unload backend
-                    page._backend.unload()
-
                 yield page
diff --git a/docling/models/rapid_ocr_model.py b/docling/models/rapid_ocr_model.py
index 5882ffc7..fa3fbedf 100644
--- a/docling/models/rapid_ocr_model.py
+++ b/docling/models/rapid_ocr_model.py
@@ -59,6 +59,7 @@ class RapidOcrModel(BaseOcrModel):
                 det_model_path=self.options.det_model_path,
                 cls_model_path=self.options.cls_model_path,
                 rec_model_path=self.options.rec_model_path,
+                rec_keys_path=self.options.rec_keys_path,
             )
 
     def __call__(
diff --git a/docling/models/table_structure_model.py b/docling/models/table_structure_model.py
index 1f4fbc7e..f17cbed0 100644
--- a/docling/models/table_structure_model.py
+++ b/docling/models/table_structure_model.py
@@ -209,12 +209,16 @@ class TableStructureModel(BasePageModel):
                                     tc.bbox = tc.bbox.scaled(1 / self.scale)
                                 table_cells.append(tc)
 
+                            assert "predict_details" in table_out
+
                             # Retrieving cols/rows, after post processing:
-                            num_rows = table_out["predict_details"]["num_rows"]
-                            num_cols = table_out["predict_details"]["num_cols"]
-                            otsl_seq = table_out["predict_details"]["prediction"][
-                                "rs_seq"
-                            ]
+                            num_rows = table_out["predict_details"].get("num_rows", 0)
+                            num_cols = table_out["predict_details"].get("num_cols", 0)
+                            otsl_seq = (
+                                table_out["predict_details"]
+                                .get("prediction", {})
+                                .get("rs_seq", [])
+                            )
 
                             tbl = Table(
                                 otsl_seq=otsl_seq,
diff --git a/docling/models/tesseract_ocr_cli_model.py b/docling/models/tesseract_ocr_cli_model.py
index 16e1629d..cdc5671d 100644
--- a/docling/models/tesseract_ocr_cli_model.py
+++ b/docling/models/tesseract_ocr_cli_model.py
@@ -4,7 +4,7 @@ import logging
 import os
 import tempfile
 from subprocess import DEVNULL, PIPE, Popen
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, List, Optional, Tuple
 
 import pandas as pd
 from docling_core.types.doc import BoundingBox, CoordOrigin
@@ -14,13 +14,13 @@ from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import TesseractCliOcrOptions
 from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
+from docling.utils.ocr_utils import map_tesseract_script
 from docling.utils.profiling import TimeRecorder
 
 _log = logging.getLogger(__name__)
 
 
 class TesseractOcrCliModel(BaseOcrModel):
-
     def __init__(self, enabled: bool, options: TesseractCliOcrOptions):
         super().__init__(enabled=enabled, options=options)
         self.options: TesseractCliOcrOptions
@@ -29,10 +29,13 @@ class TesseractOcrCliModel(BaseOcrModel):
 
         self._name: Optional[str] = None
         self._version: Optional[str] = None
+        self._tesseract_languages: Optional[List[str]] = None
+        self._script_prefix: Optional[str] = None
 
         if self.enabled:
             try:
                 self._get_name_and_version()
+                self._set_languages_and_prefix()
 
             except Exception as exc:
                 raise RuntimeError(
@@ -74,12 +77,20 @@ class TesseractOcrCliModel(BaseOcrModel):
         return name, version
 
     def _run_tesseract(self, ifilename: str):
-
+        r"""
+        Run tesseract CLI
+        """
         cmd = [self.options.tesseract_cmd]
 
-        if self.options.lang is not None and len(self.options.lang) > 0:
+        if "auto" in self.options.lang:
+            lang = self._detect_language(ifilename)
+            if lang is not None:
+                cmd.append("-l")
+                cmd.append(lang)
+        elif self.options.lang is not None and len(self.options.lang) > 0:
             cmd.append("-l")
             cmd.append("+".join(self.options.lang))
+
         if self.options.path is not None:
             cmd.append("--tessdata-dir")
             cmd.append(self.options.path)
@@ -107,6 +118,63 @@ class TesseractOcrCliModel(BaseOcrModel):
 
         return df_filtered
 
+    def _detect_language(self, ifilename: str):
+        r"""
+        Run tesseract in PSM 0 mode to detect the language
+        """
+        assert self._tesseract_languages is not None
+
+        cmd = [self.options.tesseract_cmd]
+        cmd.extend(["--psm", "0", "-l", "osd", ifilename, "stdout"])
+        _log.info("command: {}".format(" ".join(cmd)))
+        proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL)
+        output, _ = proc.communicate()
+        decoded_data = output.decode("utf-8")
+        df = pd.read_csv(
+            io.StringIO(decoded_data), sep=":", header=None, names=["key", "value"]
+        )
+        scripts = df.loc[df["key"] == "Script"].value.tolist()
+        if len(scripts) == 0:
+            _log.warning("Tesseract cannot detect the script of the page")
+            return None
+
+        script = map_tesseract_script(scripts[0].strip())
+        lang = f"{self._script_prefix}{script}"
+
+        # Check if the detected language has been installed
+        if lang not in self._tesseract_languages:
+            msg = f"Tesseract detected the script '{script}' and language '{lang}'."
+            msg += " However this language is not installed in your system and will be ignored."
+            _log.warning(msg)
+            return None
+
+        _log.debug(
+            f"Using tesseract model for the detected script '{script}' and language '{lang}'"
+        )
+        return lang
+
+    def _set_languages_and_prefix(self):
+        r"""
+        Read and set the languages installed in tesseract and decide the script prefix
+        """
+        # Get all languages
+        cmd = [self.options.tesseract_cmd]
+        cmd.append("--list-langs")
+        _log.info("command: {}".format(" ".join(cmd)))
+        proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL)
+        output, _ = proc.communicate()
+        decoded_data = output.decode("utf-8")
+        df = pd.read_csv(io.StringIO(decoded_data), header=None)
+        self._tesseract_languages = df[0].tolist()[1:]
+
+        # Decide the script prefix
+        if any([l.startswith("script/") for l in self._tesseract_languages]):
+            script_prefix = "script/"
+        else:
+            script_prefix = ""
+
+        self._script_prefix = script_prefix
+
     def __call__(
         self, conv_res: ConversionResult, page_batch: Iterable[Page]
     ) -> Iterable[Page]:
@@ -121,7 +189,6 @@ class TesseractOcrCliModel(BaseOcrModel):
                 yield page
             else:
                 with TimeRecorder(conv_res, "ocr"):
-
                     ocr_rects = self.get_ocr_rects(page)
 
                     all_ocr_cells = []
diff --git a/docling/models/tesseract_ocr_model.py b/docling/models/tesseract_ocr_model.py
index b2bd358b..5b70155e 100644
--- a/docling/models/tesseract_ocr_model.py
+++ b/docling/models/tesseract_ocr_model.py
@@ -8,6 +8,7 @@ from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import TesseractOcrOptions
 from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
+from docling.utils.ocr_utils import map_tesseract_script
 from docling.utils.profiling import TimeRecorder
 
 _log = logging.getLogger(__name__)
@@ -20,6 +21,7 @@ class TesseractOcrModel(BaseOcrModel):
 
         self.scale = 3  # multiplier for 72 dpi == 216 dpi.
         self.reader = None
+        self.osd_reader = None
 
         if self.enabled:
             install_errmsg = (
@@ -47,27 +49,38 @@ class TesseractOcrModel(BaseOcrModel):
             except:
                 raise ImportError(install_errmsg)
 
-            _, tesserocr_languages = tesserocr.get_languages()
-            if not tesserocr_languages:
+            _, self._tesserocr_languages = tesserocr.get_languages()
+            if not self._tesserocr_languages:
                 raise ImportError(missing_langs_errmsg)
 
             # Initialize the tesseractAPI
             _log.debug("Initializing TesserOCR: %s", tesseract_version)
             lang = "+".join(self.options.lang)
+
+            self.script_readers: dict[str, tesserocr.PyTessBaseAPI] = {}
+
+            if any([l.startswith("script/") for l in self._tesserocr_languages]):
+                self.script_prefix = "script/"
+            else:
+                self.script_prefix = ""
+
+            tesserocr_kwargs = {
+                "psm": tesserocr.PSM.AUTO,
+                "init": True,
+                "oem": tesserocr.OEM.DEFAULT,
+            }
+
             if self.options.path is not None:
-                self.reader = tesserocr.PyTessBaseAPI(
-                    path=self.options.path,
-                    lang=lang,
-                    psm=tesserocr.PSM.AUTO,
-                    init=True,
-                    oem=tesserocr.OEM.DEFAULT,
+                tesserocr_kwargs["path"] = self.options.path
+
+            if lang == "auto":
+                self.reader = tesserocr.PyTessBaseAPI(**tesserocr_kwargs)
+                self.osd_reader = tesserocr.PyTessBaseAPI(
+                    **{"lang": "osd", "psm": tesserocr.PSM.OSD_ONLY} | tesserocr_kwargs
                 )
             else:
                 self.reader = tesserocr.PyTessBaseAPI(
-                    lang=lang,
-                    psm=tesserocr.PSM.AUTO,
-                    init=True,
-                    oem=tesserocr.OEM.DEFAULT,
+                    **{"lang": lang} | tesserocr_kwargs,
                 )
             self.reader_RIL = tesserocr.RIL
 
@@ -75,11 +88,12 @@ class TesseractOcrModel(BaseOcrModel):
         if self.reader is not None:
             # Finalize the tesseractAPI
             self.reader.End()
+        for script in self.script_readers:
+            self.script_readers[script].End()
 
     def __call__(
         self, conv_res: ConversionResult, page_batch: Iterable[Page]
     ) -> Iterable[Page]:
-
         if not self.enabled:
             yield from page_batch
             return
@@ -90,8 +104,8 @@ class TesseractOcrModel(BaseOcrModel):
                 yield page
             else:
                 with TimeRecorder(conv_res, "ocr"):
-
                     assert self.reader is not None
+                    assert self._tesserocr_languages is not None
 
                     ocr_rects = self.get_ocr_rects(page)
 
@@ -104,22 +118,56 @@ class TesseractOcrModel(BaseOcrModel):
                             scale=self.scale, cropbox=ocr_rect
                         )
 
-                        # Retrieve text snippets with their bounding boxes
-                        self.reader.SetImage(high_res_image)
-                        boxes = self.reader.GetComponentImages(
+                        local_reader = self.reader
+                        if "auto" in self.options.lang:
+                            assert self.osd_reader is not None
+
+                            self.osd_reader.SetImage(high_res_image)
+                            osd = self.osd_reader.DetectOrientationScript()
+
+                            # No text, probably
+                            if osd is None:
+                                continue
+
+                            script = osd["script_name"]
+                            script = map_tesseract_script(script)
+                            lang = f"{self.script_prefix}{script}"
+
+                            # Check if the detected languge is present in the system
+                            if lang not in self._tesserocr_languages:
+                                msg = f"Tesseract detected the script '{script}' and language '{lang}'."
+                                msg += " However this language is not installed in your system and will be ignored."
+                                _log.warning(msg)
+                            else:
+                                if script not in self.script_readers:
+                                    import tesserocr
+
+                                    self.script_readers[script] = (
+                                        tesserocr.PyTessBaseAPI(
+                                            path=self.reader.GetDatapath(),
+                                            lang=lang,
+                                            psm=tesserocr.PSM.AUTO,
+                                            init=True,
+                                            oem=tesserocr.OEM.DEFAULT,
+                                        )
+                                    )
+                                local_reader = self.script_readers[script]
+
+                        local_reader.SetImage(high_res_image)
+                        boxes = local_reader.GetComponentImages(
                             self.reader_RIL.TEXTLINE, True
                         )
 
                         cells = []
                         for ix, (im, box, _, _) in enumerate(boxes):
                             # Set the area of interest. Tesseract uses Bottom-Left for the origin
-                            self.reader.SetRectangle(
+                            local_reader.SetRectangle(
                                 box["x"], box["y"], box["w"], box["h"]
                             )
 
                             # Extract text within the bounding box
-                            text = self.reader.GetUTF8Text().strip()
-                            confidence = self.reader.MeanTextConf()
+                            text = local_reader.GetUTF8Text().strip()
+                            confidence = local_reader.MeanTextConf()
                             left = box["x"] / self.scale
                             bottom = box["y"] / self.scale
                             right = (box["x"] + box["w"]) / self.scale
diff --git a/docling/pipeline/base_pipeline.py b/docling/pipeline/base_pipeline.py
index c75faaec..89aedf8e 100644
--- a/docling/pipeline/base_pipeline.py
+++ b/docling/pipeline/base_pipeline.py
@@ -3,7 +3,7 @@ import logging
 import time
 import traceback
 from abc import ABC, abstractmethod
-from typing import Callable, Iterable, List
+from typing import Any, Callable, Iterable, List
 
 from docling_core.types.doc import DoclingDocument, NodeItem
 
@@ -18,7 +18,7 @@ from docling.datamodel.base_models import (
 from docling.datamodel.document import ConversionResult, InputDocument
 from docling.datamodel.pipeline_options import PipelineOptions
 from docling.datamodel.settings import settings
-from docling.models.base_model import BaseEnrichmentModel
+from docling.models.base_model import GenericEnrichmentModel
 from docling.utils.profiling import ProfilingScope, TimeRecorder
 from docling.utils.utils import chunkify
 
@@ -28,8 +28,9 @@ _log = logging.getLogger(__name__)
 class BasePipeline(ABC):
     def __init__(self, pipeline_options: PipelineOptions):
         self.pipeline_options = pipeline_options
+        self.keep_images = False
         self.build_pipe: List[Callable] = []
-        self.enrichment_pipe: List[BaseEnrichmentModel] = []
+        self.enrichment_pipe: List[GenericEnrichmentModel[Any]] = []
 
     def execute(self, in_doc: InputDocument, raises_on_error: bool) -> ConversionResult:
         conv_res = ConversionResult(input=in_doc)
@@ -40,7 +41,7 @@ class BasePipeline(ABC):
                 conv_res, "pipeline_total", scope=ProfilingScope.DOCUMENT
             ):
                 # These steps are building and assembling the structure of the
-                # output DoclingDocument
+                # output DoclingDocument.
                 conv_res = self._build_document(conv_res)
                 conv_res = self._assemble_document(conv_res)
                 # From this stage, all operations should rely only on conv_res.output
@@ -50,6 +51,8 @@ class BasePipeline(ABC):
             conv_res.status = ConversionStatus.FAILURE
             if raises_on_error:
                 raise e
+        finally:
+            self._unload(conv_res)
 
         return conv_res
 
@@ -62,21 +65,22 @@ class BasePipeline(ABC):
 
     def _enrich_document(self, conv_res: ConversionResult) -> ConversionResult:
 
-        def _filter_elements(
-            doc: DoclingDocument, model: BaseEnrichmentModel
+        def _prepare_elements(
+            conv_res: ConversionResult, model: GenericEnrichmentModel[Any]
         ) -> Iterable[NodeItem]:
-            for element, _level in doc.iterate_items():
-                if model.is_processable(doc=doc, element=element):
-                    yield element
+            for doc_element, _level in conv_res.document.iterate_items():
+                prepared_element = model.prepare_element(
+                    conv_res=conv_res, element=doc_element
+                )
+                if prepared_element is not None:
+                    yield prepared_element
 
         with TimeRecorder(conv_res, "doc_enrich", scope=ProfilingScope.DOCUMENT):
             for model in self.enrichment_pipe:
                 for element_batch in chunkify(
-                    _filter_elements(conv_res.document, model),
+                    _prepare_elements(conv_res, model),
                     settings.perf.elements_batch_size,
                 ):
-                    # TODO: currently we assume the element itself is modified, because
-                    # we don't have an interface to save the element back to the document
                     for element in model(
                         doc=conv_res.document, element_batch=element_batch
                     ):  # Must exhaust!
@@ -88,6 +92,9 @@ class BasePipeline(ABC):
     def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
         pass
 
+    def _unload(self, conv_res: ConversionResult):
+        pass
+
     @classmethod
     @abstractmethod
     def get_default_options(cls) -> PipelineOptions:
@@ -107,6 +114,10 @@ class BasePipeline(ABC):
 
 class PaginatedPipeline(BasePipeline):  # TODO this is a bad name.
 
+    def __init__(self, pipeline_options: PipelineOptions):
+        super().__init__(pipeline_options)
+        self.keep_backend = False
+
     def _apply_on_pages(
         self, conv_res: ConversionResult, page_batch: Iterable[Page]
     ) -> Iterable[Page]:
@@ -130,7 +141,9 @@ class PaginatedPipeline(BasePipeline):  # TODO this is a bad name.
         with TimeRecorder(conv_res, "doc_build", scope=ProfilingScope.DOCUMENT):
 
             for i in range(0, conv_res.input.page_count):
-                conv_res.pages.append(Page(page_no=i))
+                start_page, end_page = conv_res.input.limits.page_range
+                if (start_page - 1) <= i <= (end_page - 1):
+                    conv_res.pages.append(Page(page_no=i))
 
             try:
                 # Iterate batches of pages (page_batch_size) in the doc
@@ -148,7 +161,14 @@ class PaginatedPipeline(BasePipeline):  # TODO this is a bad name.
                     pipeline_pages = self._apply_on_pages(conv_res, init_pages)
 
                     for p in pipeline_pages:  # Must exhaust!
-                        pass
+
+                        # Cleanup cached images
+                        if not self.keep_images:
+                            p._image_cache = {}
+
+                        # Cleanup page backends
+                        if not self.keep_backend and p._backend is not None:
+                            p._backend.unload()
 
                     end_batch_time = time.monotonic()
                     total_elapsed_time += end_batch_time - start_batch_time
@@ -177,10 +197,15 @@ class PaginatedPipeline(BasePipeline):  # TODO this is a bad name.
                 )
                 raise e
 
-            finally:
-                # Always unload the PDF backend, even in case of failure
-                if conv_res.input._backend:
-                    conv_res.input._backend.unload()
+        return conv_res
+
+    def _unload(self, conv_res: ConversionResult) -> ConversionResult:
+        for page in conv_res.pages:
+            if page._backend is not None:
+                page._backend.unload()
+
+        if conv_res.input._backend:
+            conv_res.input._backend.unload()
 
         return conv_res
 
diff --git a/docling/pipeline/standard_pdf_pipeline.py b/docling/pipeline/standard_pdf_pipeline.py
index 2f8c1421..fe2201d6 100644
--- a/docling/pipeline/standard_pdf_pipeline.py
+++ b/docling/pipeline/standard_pdf_pipeline.py
@@ -18,6 +18,11 @@ from docling.datamodel.pipeline_options import (
     TesseractOcrOptions,
 )
 from docling.models.base_ocr_model import BaseOcrModel
+from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
+from docling.models.document_picture_classifier import (
+    DocumentPictureClassifier,
+    DocumentPictureClassifierOptions,
+)
 from docling.models.ds_glm_model import GlmModel, GlmOptions
 from docling.models.easyocr_model import EasyOcrModel
 from docling.models.layout_model import LayoutModel
@@ -50,7 +55,7 @@ class StandardPdfPipeline(PaginatedPipeline):
         else:
             self.artifacts_path = Path(pipeline_options.artifacts_path)
 
-        keep_images = (
+        self.keep_images = (
             self.pipeline_options.generate_page_images
             or self.pipeline_options.generate_picture_images
             or self.pipeline_options.generate_table_images
@@ -87,13 +92,37 @@ class StandardPdfPipeline(PaginatedPipeline):
                 accelerator_options=pipeline_options.accelerator_options,
             ),
             # Page assemble
-            PageAssembleModel(options=PageAssembleOptions(keep_images=keep_images)),
+            PageAssembleModel(options=PageAssembleOptions()),
         ]
 
         self.enrichment_pipe = [
             # Other models working on `NodeItem` elements in the DoclingDocument
+            # Code Formula Enrichment Model
+            CodeFormulaModel(
+                enabled=pipeline_options.do_code_enrichment
+                or pipeline_options.do_formula_enrichment,
+                artifacts_path=pipeline_options.artifacts_path,
+                options=CodeFormulaModelOptions(
+                    do_code_enrichment=pipeline_options.do_code_enrichment,
+                    do_formula_enrichment=pipeline_options.do_formula_enrichment,
+                ),
+                accelerator_options=pipeline_options.accelerator_options,
+            ),
+            # Document Picture Classifier
+            DocumentPictureClassifier(
+                enabled=pipeline_options.do_picture_classification,
+                artifacts_path=pipeline_options.artifacts_path,
+                options=DocumentPictureClassifierOptions(),
+                accelerator_options=pipeline_options.accelerator_options,
+            ),
         ]
 
+        if (
+            self.pipeline_options.do_formula_enrichment
+            or self.pipeline_options.do_code_enrichment
+        ):
+            self.keep_backend = True
+
     @staticmethod
     def download_models_hf(
         local_dir: Optional[Path] = None, force: bool = False
diff --git a/docling/utils/glm_utils.py b/docling/utils/glm_utils.py
index 96d514eb..c3c43536 100644
--- a/docling/utils/glm_utils.py
+++ b/docling/utils/glm_utils.py
@@ -15,6 +15,7 @@ from docling_core.types.doc import (
     TableCell,
     TableData,
 )
+from docling_core.types.doc.document import ContentLayer
 
 
 def resolve_item(paths, obj):
@@ -270,7 +271,6 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument:
             container_el = doc.add_group(label=group_label)
 
             _add_child_elements(container_el, doc, obj, pelem)
-
         elif "text" in obj:
             text = obj["text"][span_i:span_j]
 
@@ -304,6 +304,14 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument:
                 current_list = None
 
                 doc.add_heading(text=text, prov=prov)
+            elif label == DocItemLabel.CODE:
+                current_list = None
+
+                doc.add_code(text=text, prov=prov)
+            elif label == DocItemLabel.FORMULA:
+                current_list = None
+
+                doc.add_text(label=DocItemLabel.FORMULA, text="", orig=text, prov=prov)
             elif label in [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]:
                 current_list = None
 
@@ -311,7 +319,7 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument:
                     label=DocItemLabel(name_label),
                     text=text,
                     prov=prov,
-                    parent=doc.furniture,
+                    content_layer=ContentLayer.FURNITURE,
                 )
             else:
                 current_list = None
diff --git a/docling/utils/ocr_utils.py b/docling/utils/ocr_utils.py
new file mode 100644
index 00000000..59503f1f
--- /dev/null
+++ b/docling/utils/ocr_utils.py
@@ -0,0 +1,9 @@
+def map_tesseract_script(script: str) -> str:
+    r""" """
+    if script == "Katakana" or script == "Hiragana":
+        script = "Japanese"
+    elif script == "Han":
+        script = "HanS"
+    elif script == "Korean":
+        script = "Hangul"
+    return script
diff --git a/docling/utils/visualization.py b/docling/utils/visualization.py
new file mode 100644
index 00000000..465b7749
--- /dev/null
+++ b/docling/utils/visualization.py
@@ -0,0 +1,80 @@
+from docling_core.types.doc import DocItemLabel
+from PIL import Image, ImageDraw, ImageFont
+from PIL.ImageFont import FreeTypeFont
+
+from docling.datamodel.base_models import Cluster
+
+
+def draw_clusters(
+    image: Image.Image, clusters: list[Cluster], scale_x: float, scale_y: float
+) -> None:
+    """
+    Draw clusters on an image
+    """
+    draw = ImageDraw.Draw(image, "RGBA")
+    # Create a smaller font for the labels
+    font: ImageFont.ImageFont | FreeTypeFont
+    try:
+        font = ImageFont.truetype("arial.ttf", 12)
+    except OSError:
+        # Fallback to default font if arial is not available
+        font = ImageFont.load_default()
+    for c_tl in clusters:
+        all_clusters = [c_tl, *c_tl.children]
+        for c in all_clusters:
+            # Draw cells first (underneath)
+            cell_color = (0, 0, 0, 40)  # Transparent black for cells
+            for tc in c.cells:
+                cx0, cy0, cx1, cy1 = tc.bbox.as_tuple()
+                cx0 *= scale_x
+                cx1 *= scale_x
+                cy0 *= scale_x
+                cy1 *= scale_y
+
+                draw.rectangle(
+                    [(cx0, cy0), (cx1, cy1)],
+                    outline=None,
+                    fill=cell_color,
+                )
+            # Draw cluster rectangle
+            x0, y0, x1, y1 = c.bbox.as_tuple()
+            x0 *= scale_x
+            x1 *= scale_x
+            y0 *= scale_x
+            y1 *= scale_y
+
+            cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70)
+            cluster_outline_color = (
+                *list(DocItemLabel.get_color(c.label)),
+                255,
+            )
+            draw.rectangle(
+                [(x0, y0), (x1, y1)],
+                outline=cluster_outline_color,
+                fill=cluster_fill_color,
+            )
+            # Add label name and confidence
+            label_text = f"{c.label.name} ({c.confidence:.2f})"
+            # Create semi-transparent background for text
+            text_bbox = draw.textbbox((x0, y0), label_text, font=font)
+            text_bg_padding = 2
+            draw.rectangle(
+                [
+                    (
+                        text_bbox[0] - text_bg_padding,
+                        text_bbox[1] - text_bg_padding,
+                    ),
+                    (
+                        text_bbox[2] + text_bg_padding,
+                        text_bbox[3] + text_bg_padding,
+                    ),
+                ],
+                fill=(255, 255, 255, 180),  # Semi-transparent white
+            )
+            # Draw text
+            draw.text(
+                (x0, y0),
+                label_text,
+                fill=(0, 0, 0, 255),  # Solid black
+                font=font,
+            )
diff --git a/docs/concepts/chunking.md b/docs/concepts/chunking.md
index bed8bce3..94ae387e 100644
--- a/docs/concepts/chunking.md
+++ b/docs/concepts/chunking.md
@@ -54,12 +54,12 @@ tokens), &
 chunks with same headings & captions) — users can opt out of this step via param
 `merge_peers` (by default `True`)
 
-👉 Example: see  [here](../../examples/hybrid_chunking).
+👉 Example: see  [here](../examples/hybrid_chunking.ipynb).
 
 ## Hierarchical Chunker
 
 The `HierarchicalChunker` implementation uses the document structure information from
-the [`DoclingDocument`](../docling_document) to create one chunk for each individual
+the [`DoclingDocument`](./docling_document.md) to create one chunk for each individual
 detected document element, by default only merging together list items (can be opted out
 via param `merge_list_items`). It also takes care of attaching all relevant document
 metadata, including headers and captions.
diff --git a/docs/examples/backend_xml_rag.ipynb b/docs/examples/backend_xml_rag.ipynb
new file mode 100644
index 00000000..78c603c8
--- /dev/null
+++ b/docs/examples/backend_xml_rag.ipynb
@@ -0,0 +1,1057 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/DS4SD/docling/blob/main/docs/examples/backend_xml_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conversion of custom XML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Step | Tech | Execution | \n",
+    "| --- | --- | --- |\n",
+    "| Embedding | Hugging Face / Sentence Transformers | 💻 Local |\n",
+    "| Vector store | Milvus | 💻 Local |\n",
+    "| Gen AI | Hugging Face Inference API | 🌐 Remote | "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is an example of using [Docling](https://ds4sd.github.io/docling/) for converting structured data (XML) into a unified document\n",
+    "representation format, `DoclingDocument`, and leverage its riched structured content for RAG applications.\n",
+    "\n",
+    "Data used in this example consist of patents from the [United States Patent and Trademark Office (USPTO)](https://www.uspto.gov/) and medical\n",
+    "articles from [PubMed Central® (PMC)](https://pmc.ncbi.nlm.nih.gov/).\n",
+    "\n",
+    "In this notebook, we accomplish the following:\n",
+    "- [Simple conversion](#simple-conversion) of supported XML files in a nutshell\n",
+    "- An [end-to-end application](#end-to-end-application) using public collections of XML files supported by Docling\n",
+    "  - [Setup](#setup) the API access for generative AI\n",
+    "  - [Fetch the data](#fetch-the-data) from USPTO and PubMed Central® sites, using Docling custom backends\n",
+    "  - [Parse, chunk, and index](#parse-chunk-and-index) the documents in a vector database\n",
+    "  - [Perform RAG](#question-answering-with-rag) using [LlamaIndex Docling extension](../../integrations/llamaindex/)\n",
+    "\n",
+    "For more details on document chunking with Docling, refer to the [Chunking](../../concepts/chunking/) documentation. For RAG with Docling and LlamaIndex, also check the example [RAG with LlamaIndex](../rag_llamaindex/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple conversion\n",
+    "\n",
+    "The XML file format defines and stores data in a format that is both human-readable and machine-readable.\n",
+    "Because of this flexibility, Docling requires custom backend processors to interpret XML definitions and convert them into `DoclingDocument` objects.\n",
+    "\n",
+    "Some public data collections in XML format are already supported by Docling (USTPO patents and PMC articles). In these cases, the document conversion is straightforward and the same as with any other supported format, such as PDF or HTML. The execution example in [Simple Conversion](../minimal/) is the recommended usage of Docling for a single file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ConversionStatus.SUCCESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "from docling.document_converter import DocumentConverter\n",
+    "\n",
+    "# a sample PMC article:\n",
+    "source = \"../../tests/data/pubmed/elife-56337.nxml\"\n",
+    "converter = DocumentConverter()\n",
+    "result = converter.convert(source)\n",
+    "print(result.status)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the document is converted, it can be exported to any format supported by Docling. For instance, to markdown (showing here the first lines only):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage\n",
+      "\n",
+      "Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States\n",
+      "\n",
+      "## Abstract\n",
+      "\n",
+      "The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "md_doc = result.document.export_to_markdown()\n",
+    "\n",
+    "delim = \"\\n\"\n",
+    "print(delim.join(md_doc.split(delim)[:8]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the XML file is not supported, a `ConversionError` message will be raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Input document docling_test.xml does not match any allowed format.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File format not allowed: docling_test.xml\n"
+     ]
+    }
+   ],
+   "source": [
+    "from io import BytesIO\n",
+    "\n",
+    "from docling.datamodel.base_models import DocumentStream\n",
+    "from docling.exceptions import ConversionError\n",
+    "\n",
+    "xml_content = (\n",
+    "    b'<?xml version=\"1.0\" encoding=\"UTF-8\"?><!DOCTYPE docling_test SYSTEM '\n",
+    "    b'\"test.dtd\"><docling>Random content</docling>'\n",
+    ")\n",
+    "stream = DocumentStream(name=\"docling_test.xml\", stream=BytesIO(xml_content))\n",
+    "try:\n",
+    "    result = converter.convert(stream)\n",
+    "except ConversionError as ce:\n",
+    "    print(ce)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can always refer to the [Usage](../../usage/#supported-formats) documentation page for a list of supported formats."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## End-to-end application\n",
+    "\n",
+    "This section describes a step-by-step application for processing XML files from supported public collections and use them for question-answering."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Requirements can be installed as shown below. The `--no-warn-conflicts` argument is meant for Colab's pre-populated Python environment, feel free to remove for stricter usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -q --progress-bar off --no-warn-conflicts llama-index-core llama-index-readers-docling llama-index-node-parser-docling llama-index-embeddings-huggingface llama-index-llms-huggingface-api llama-index-vector-stores-milvus llama-index-readers-file python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook uses HuggingFace's Inference API. For an increased LLM quota, a token can be provided via the environment variable `HF_TOKEN`.\n",
+    "\n",
+    "If you're running this notebook in Google Colab, make sure you [add](https://medium.com/@parthdasawant/how-to-use-secrets-in-google-colab-450c38e3ec75) your API key as a secret."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from warnings import filterwarnings\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "\n",
+    "def _get_env_from_colab_or_os(key):\n",
+    "    try:\n",
+    "        from google.colab import userdata\n",
+    "\n",
+    "        try:\n",
+    "            return userdata.get(key)\n",
+    "        except userdata.SecretNotFoundError:\n",
+    "            pass\n",
+    "    except ImportError:\n",
+    "        pass\n",
+    "    return os.getenv(key)\n",
+    "\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now define the main parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from tempfile import mkdtemp\n",
+    "\n",
+    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
+    "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
+    "\n",
+    "EMBED_MODEL_ID = \"BAAI/bge-small-en-v1.5\"\n",
+    "EMBED_MODEL = HuggingFaceEmbedding(model_name=EMBED_MODEL_ID)\n",
+    "TEMP_DIR = Path(mkdtemp())\n",
+    "MILVUS_URI = str(TEMP_DIR / \"docling.db\")\n",
+    "GEN_MODEL = HuggingFaceInferenceAPI(\n",
+    "    token=_get_env_from_colab_or_os(\"HF_TOKEN\"),\n",
+    "    model_name=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
+    ")\n",
+    "embed_dim = len(EMBED_MODEL.get_text_embedding(\"hi\"))\n",
+    "# https://github.com/huggingface/transformers/issues/5486:\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fetch the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we will use XML data from collections supported by Docling:\n",
+    "- Medical articles from the [PubMed Central® (PMC)](https://pmc.ncbi.nlm.nih.gov/). They are available in an [FTP server](https://ftp.ncbi.nlm.nih.gov/pub/pmc/) as `.tar.gz` files. Each file contains the full article data in XML format, among other supplementary files like images or spreadsheets.\n",
+    "- Patents from the [United States Patent and Trademark Office](https://www.uspto.gov/). They are available in the [Bulk Data Storage System (BDSS)](https://bulkdata.uspto.gov/) as zip files. Each zip file may contain several patents in XML format.\n",
+    "\n",
+    "The raw files will be downloaded form the source and saved in a temporary directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### PMC articles\n",
+    "\n",
+    "The [OA file](https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_file_list.csv) is a manifest file of all the PMC articles, including the URL path to download the source files. In this notebook we will use as example the article [Pathogens spread by high-altitude windborne mosquitoes](https://pmc.ncbi.nlm.nih.gov/articles/PMC11703268/), which is available in the archive file [PMC11703268.tar.gz](https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz...\n",
+      "Extracting and storing the XML file containing the article text...\n",
+      "Stored XML file nihpp-2024.12.26.630351v1.nxml\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tarfile\n",
+    "from io import BytesIO\n",
+    "\n",
+    "import requests\n",
+    "\n",
+    "# PMC article PMC11703268\n",
+    "url: str = \"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/e3/6b/PMC11703268.tar.gz\"\n",
+    "\n",
+    "print(f\"Downloading {url}...\")\n",
+    "buf = BytesIO(requests.get(url).content)\n",
+    "print(\"Extracting and storing the XML file containing the article text...\")\n",
+    "with tarfile.open(fileobj=buf, mode=\"r:gz\") as tar_file:\n",
+    "    for tarinfo in tar_file:\n",
+    "        if tarinfo.isreg():\n",
+    "            file_path = Path(tarinfo.name)\n",
+    "            if file_path.suffix == \".nxml\":\n",
+    "                with open(TEMP_DIR / file_path.name, \"wb\") as file_obj:\n",
+    "                    file_obj.write(tar_file.extractfile(tarinfo).read())\n",
+    "                print(f\"Stored XML file {file_path.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### USPTO patents\n",
+    "\n",
+    "Since each USPTO file is a concatenation of several patents, we need to split its content into valid XML pieces. The following code downloads a sample zip file, split its content in sections, and dumps each section as an XML file. For simplicity, this pipeline is shown here in a sequential manner, but it could be parallelized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "# Patent grants from December 17-23, 2024\n",
+    "url: str = (\n",
+    "    \"https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/2024/ipg241217.zip\"\n",
+    ")\n",
+    "XML_SPLITTER: str = '<?xml version=\"1.0\"'\n",
+    "doc_num: int = 0\n",
+    "\n",
+    "print(f\"Downloading {url}...\")\n",
+    "buf = BytesIO(requests.get(url).content)\n",
+    "print(f\"Parsing zip file, splitting into XML sections, and exporting to files...\")\n",
+    "with zipfile.ZipFile(buf) as zf:\n",
+    "    res = zf.testzip()\n",
+    "    if res:\n",
+    "        print(\"Error validating zip file\")\n",
+    "    else:\n",
+    "        with zf.open(zf.namelist()[0]) as xf:\n",
+    "            is_patent = False\n",
+    "            patent_buffer = BytesIO()\n",
+    "            for xf_line in xf:\n",
+    "                decoded_line = xf_line.decode(errors=\"ignore\").rstrip()\n",
+    "                xml_index = decoded_line.find(XML_SPLITTER)\n",
+    "                if xml_index != -1:\n",
+    "                    if (\n",
+    "                        xml_index > 0\n",
+    "                    ):  # cases like </sequence-cwu><?xml version=\"1.0\"...\n",
+    "                        patent_buffer.write(xf_line[:xml_index])\n",
+    "                        patent_buffer.write(b\"\\r\\n\")\n",
+    "                        xf_line = xf_line[xml_index:]\n",
+    "                    if patent_buffer.getbuffer().nbytes > 0 and is_patent:\n",
+    "                        doc_num += 1\n",
+    "                        patent_id = f\"ipg241217-{doc_num}\"\n",
+    "                        with open(TEMP_DIR / f\"{patent_id}.xml\", \"wb\") as file_obj:\n",
+    "                            file_obj.write(patent_buffer.getbuffer())\n",
+    "                    is_patent = False\n",
+    "                    patent_buffer = BytesIO()\n",
+    "                elif decoded_line.startswith(\"<!DOCTYPE\"):\n",
+    "                    is_patent = True\n",
+    "                patent_buffer.write(xf_line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fetched and exported 4014 documents.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Fetched and exported {doc_num} documents.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the backend converter (optional)\n",
+    "\n",
+    "- The custom backend converters `PubMedDocumentBackend` and `PatentUsptoDocumentBackend` aim at handling the parsing of PMC articles and USPTO patents, respectively.\n",
+    "- As any other backends, you can leverage the function `is_valid()` to check if the input document is supported by the this backend.\n",
+    "- Note that some XML sections in the original USPTO zip file may not represent patents, like sequence listings, and therefore they will show as invalid by the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Document nihpp-2024.12.26.630351v1.nxml is a valid PMC article? True\n",
+      "Document ipg241217-1.xml is a valid patent? True\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3964d1ff30f74588a2f6b53ca8865a9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4014 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 3928 patents out of 4014 XML files.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "from docling.backend.xml.pubmed_backend import PubMedDocumentBackend\n",
+    "from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend\n",
+    "from docling.datamodel.base_models import InputFormat\n",
+    "from docling.datamodel.document import InputDocument\n",
+    "\n",
+    "# check PMC\n",
+    "in_doc = InputDocument(\n",
+    "    path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\",\n",
+    "    format=InputFormat.XML_PUBMED,\n",
+    "    backend=PubMedDocumentBackend,\n",
+    ")\n",
+    "backend = PubMedDocumentBackend(\n",
+    "    in_doc=in_doc, path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\"\n",
+    ")\n",
+    "print(f\"Document {in_doc.file.name} is a valid PMC article? {backend.is_valid()}\")\n",
+    "\n",
+    "# check USPTO\n",
+    "in_doc = InputDocument(\n",
+    "    path_or_stream=TEMP_DIR / \"ipg241217-1.xml\",\n",
+    "    format=InputFormat.XML_USPTO,\n",
+    "    backend=PatentUsptoDocumentBackend,\n",
+    ")\n",
+    "backend = PatentUsptoDocumentBackend(\n",
+    "    in_doc=in_doc, path_or_stream=TEMP_DIR / \"ipg241217-1.xml\"\n",
+    ")\n",
+    "print(f\"Document {in_doc.file.name} is a valid patent? {backend.is_valid()}\")\n",
+    "\n",
+    "patent_valid = 0\n",
+    "pbar = tqdm(TEMP_DIR.glob(\"*.xml\"), total=doc_num)\n",
+    "for in_path in pbar:\n",
+    "    in_doc = InputDocument(\n",
+    "        path_or_stream=in_path,\n",
+    "        format=InputFormat.XML_USPTO,\n",
+    "        backend=PatentUsptoDocumentBackend,\n",
+    "    )\n",
+    "    backend = PatentUsptoDocumentBackend(in_doc=in_doc, path_or_stream=in_path)\n",
+    "    patent_valid += int(backend.is_valid())\n",
+    "\n",
+    "print(f\"Found {patent_valid} patents out of {doc_num} XML files.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling the function `convert()` will convert the input document into a `DoclingDocument`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patent \"Semiconductor package\" has 19 claims\n"
+     ]
+    }
+   ],
+   "source": [
+    "doc = backend.convert()\n",
+    "\n",
+    "claims_sec = [item for item in doc.texts if item.text == \"CLAIMS\"][0]\n",
+    "print(f'Patent \"{doc.texts[0].text}\" has {len(claims_sec.children)} claims')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✏️ **Tip**: in general, there is no need to use the backend converters to parse USPTO or PubMed XML files. The generic `DocumentConverter` object tries to guess the input document format and applies the corresponding backend parser. The conversion shown in [Simple Conversion](#simple-conversion) is the recommended usage for the supported XML files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parse, chunk, and index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `DoclingDocument` format of the converted patents has a rich hierarchical structure, inherited from the original XML document and preserved by the Docling custom backend.\n",
+    "In this notebook, we will leverage:\n",
+    "- The `SimpleDirectoryReader` pattern to iterate over the exported XML files created in section [Fetch the data](#fetch-the-data).\n",
+    "- The LlamaIndex extensions, `DoclingReader` and `DoclingNodeParser`, to ingest the patent chunks into a Milvus vectore store.\n",
+    "- The `HierarchicalChunker` implementation, which applies a document-based hierarchical chunking, to leverage the patent structures like sections and paragraphs within sections.\n",
+    "\n",
+    "Refer to other possible implementations and usage patterns in the [Chunking](../../concepts/chunking/) documentation and the [RAG with LlamaIndex](../rag_llamaindex/) notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Set the Docling reader and the directory reader\n",
+    "\n",
+    "Note that `DoclingReader` uses Docling's `DocumentConverter` by default and therefore it will recognize the format of the XML files and leverage the `PatentUsptoDocumentBackend` automatically.\n",
+    "\n",
+    "For demonstration purposes, we limit the scope of the analysis to the first 100 patents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import SimpleDirectoryReader\n",
+    "from llama_index.readers.docling import DoclingReader\n",
+    "\n",
+    "reader = DoclingReader(export_type=DoclingReader.ExportType.JSON)\n",
+    "dir_reader = SimpleDirectoryReader(\n",
+    "    input_dir=TEMP_DIR,\n",
+    "    exclude=[\"docling.db\", \"*.nxml\"],\n",
+    "    file_extractor={\".xml\": reader},\n",
+    "    filename_as_id=True,\n",
+    "    num_files_limit=100,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Set the node parser\n",
+    "\n",
+    "Note that the `HierarchicalChunker` is the default chunking implementation of the `DoclingNodeParser`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.node_parser.docling import DoclingNodeParser\n",
+    "\n",
+    "node_parser = DoclingNodeParser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Set a local Milvus database and run the ingestion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-01-24 16:49:57,108 [DEBUG][_create_connection]: Created new connection using: 2d58fad6c63448a486c0c0ffe3b7b28c (async_milvus_client.py:600)\n",
+      "Loading files:  51%|█████     | 51/100 [00:00<00:00, 67.88file/s]Input document ipg241217-1050.xml does not match any allowed format.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to load file /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml with error: File format not allowed: /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml. Skipping...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading files: 100%|██████████| 100/100 [00:01<00:00, 58.05file/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9208639f1a4418d97267a28305d18fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Parsing nodes:   0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88026613f6f44f0c8476dceaa1cb78cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7522b8b434b54616b4cfc3d71e9556d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5879d8161c2041f5b100959e69ff9017",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "557912b5e3c741f3a06127156bc46379",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "843bb145942b449aa55fc5b8208da734",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7dba09a4aed422998e9b9c2c3a70317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/2048 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bd031356c7e4e879dcbe1d04e6c4a4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/425 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from llama_index.core import StorageContext, VectorStoreIndex\n",
+    "from llama_index.vector_stores.milvus import MilvusVectorStore\n",
+    "\n",
+    "vector_store = MilvusVectorStore(\n",
+    "    uri=MILVUS_URI,\n",
+    "    dim=embed_dim,\n",
+    "    overwrite=True,\n",
+    ")\n",
+    "\n",
+    "index = VectorStoreIndex.from_documents(\n",
+    "    documents=dir_reader.load_data(show_progress=True),\n",
+    "    transformations=[node_parser],\n",
+    "    storage_context=StorageContext.from_defaults(vector_store=vector_store),\n",
+    "    embed_model=EMBED_MODEL,\n",
+    "    show_progress=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, add the PMC article to the vector store directly from the reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<llama_index.core.indices.vector_store.base.VectorStoreIndex at 0x373a7f7d0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index.from_documents(\n",
+    "    documents=reader.load_data(TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\"),\n",
+    "    transformations=[node_parser],\n",
+    "    storage_context=StorageContext.from_defaults(vector_store=vector_store),\n",
+    "    embed_model=EMBED_MODEL,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question-answering with RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The retriever can be used to identify highly relevant documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Node ID: 5afd36c0-a739-4a88-a51c-6d0f75358db5\n",
+      "Text: The portable fitness monitoring device 102 may be a device such\n",
+      "as, for example, a mobile phone, a personal digital assistant, a music\n",
+      "file player (e.g. and MP3 player), an intelligent article for wearing\n",
+      "(e.g. a fitness monitoring garment, wrist band, or watch), a dongle\n",
+      "(e.g. a small hardware device that protects software) that includes a\n",
+      "fitn...\n",
+      "Score:  0.772\n",
+      "\n",
+      "Node ID: f294b5fd-9089-43cb-8c4e-d1095a634ff1\n",
+      "Text: US Patent Application US 20120071306 entitled “Portable\n",
+      "Multipurpose Whole Body Exercise Device” discloses a portable\n",
+      "multipurpose whole body exercise device which can be used for general\n",
+      "fitness, Pilates-type, core strengthening, therapeutic, and\n",
+      "rehabilitative exercises as well as stretching and physical therapy\n",
+      "and which includes storable acc...\n",
+      "Score:  0.749\n",
+      "\n",
+      "Node ID: 8251c7ef-1165-42e1-8c91-c99c8a711bf7\n",
+      "Text: Program products, methods, and systems for providing fitness\n",
+      "monitoring services of the present invention can include any software\n",
+      "application executed by one or more computing devices. A computing\n",
+      "device can be any type of computing device having one or more\n",
+      "processors. For example, a computing device can be a workstation,\n",
+      "mobile device (e.g., ...\n",
+      "Score:  0.744\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "retriever = index.as_retriever(similarity_top_k=3)\n",
+    "results = retriever.retrieve(\"What patents are related to fitness devices?\")\n",
+    "\n",
+    "for item in results:\n",
+    "    print(item)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the query engine, we can run the question-answering with the RAG pattern on the set of indexed documents.\n",
+    "\n",
+    "First, we can prompt the LLM directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">╭──────────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│</span> Do mosquitoes in high altitude expand viruses over large distances?                                             <span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;31m╭─\u001b[0m\u001b[1;31m───────────────────────────────────────────────────\u001b[0m\u001b[1;31m Prompt \u001b[0m\u001b[1;31m────────────────────────────────────────────────────\u001b[0m\u001b[1;31m─╮\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m Do mosquitoes in high altitude expand viruses over large distances?                                             \u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╭─────────────────────────────────────────────── Generated Content ───────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> Mosquitoes can be found at high altitudes, but their ability to transmit viruses over long distances is not     <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> primarily dependent on altitude. Mosquitoes are vectors for various diseases, such as malaria, dengue fever,    <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> and Zika virus, and their transmission range is more closely related to their movement, the presence of a host, <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> and environmental conditions that support their survival and reproduction.                                      <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> At high altitudes, the environment can be less suitable for mosquitoes due to factors such as colder            <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> temperatures, lower humidity, and stronger winds, which can limit their population size and distribution.       <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> However, some species of mosquitoes have adapted to high-altitude environments and can still transmit diseases  <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> in these areas.                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> It is possible for mosquitoes to be transported by wind or human activities to higher altitudes, but this is    <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> not a significant factor in their ability to transmit viruses over long distances. Instead, long-distance       <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> transmission of viruses is more often associated with human travel and transportation, which can rapidly spread <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> infected mosquitoes or humans to new areas, leading to the spread of disease.                                   <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;32m╭─\u001b[0m\u001b[1;32m──────────────────────────────────────────────\u001b[0m\u001b[1;32m Generated Content \u001b[0m\u001b[1;32m──────────────────────────────────────────────\u001b[0m\u001b[1;32m─╮\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m Mosquitoes can be found at high altitudes, but their ability to transmit viruses over long distances is not     \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m primarily dependent on altitude. Mosquitoes are vectors for various diseases, such as malaria, dengue fever,    \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m and Zika virus, and their transmission range is more closely related to their movement, the presence of a host, \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m and environmental conditions that support their survival and reproduction.                                      \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m                                                                                                                 \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m At high altitudes, the environment can be less suitable for mosquitoes due to factors such as colder            \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m temperatures, lower humidity, and stronger winds, which can limit their population size and distribution.       \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m However, some species of mosquitoes have adapted to high-altitude environments and can still transmit diseases  \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m in these areas.                                                                                                 \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m                                                                                                                 \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m It is possible for mosquitoes to be transported by wind or human activities to higher altitudes, but this is    \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m not a significant factor in their ability to transmit viruses over long distances. Instead, long-distance       \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m transmission of viruses is more often associated with human travel and transportation, which can rapidly spread \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m infected mosquitoes or humans to new areas, leading to the spread of disease.                                   \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from llama_index.core.base.llms.types import ChatMessage, MessageRole\n",
+    "from rich.console import Console\n",
+    "from rich.panel import Panel\n",
+    "\n",
+    "console = Console()\n",
+    "query = \"Do mosquitoes in high altitude expand viruses over large distances?\"\n",
+    "\n",
+    "usr_msg = ChatMessage(role=MessageRole.USER, content=query)\n",
+    "response = GEN_MODEL.chat(messages=[usr_msg])\n",
+    "\n",
+    "console.print(Panel(query, title=\"Prompt\", border_style=\"bold red\"))\n",
+    "console.print(\n",
+    "    Panel(\n",
+    "        response.message.content.strip(),\n",
+    "        title=\"Generated Content\",\n",
+    "        border_style=\"bold green\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can compare the response when the model is prompted with the indexed PMC article as supporting context:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╭────────────────────────────────────────── Generated Content with RAG ───────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> Yes, mosquitoes in high altitude can expand viruses over large distances. A study intercepted 1,017 female      <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> mosquitoes at altitudes of 120-290 m above ground over Mali and Ghana and screened them for infection with      <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> arboviruses, plasmodia, and filariae. The study found that 3.5% of the mosquitoes were infected with            <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> flaviviruses, and 1.1% were infectious. Additionally, the study identified 19 mosquito-borne pathogens,         <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> including three arboviruses that affect humans (dengue, West Nile, and M’Poko viruses). The study provides      <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span> compelling evidence that mosquito-borne pathogens are often spread by windborne mosquitoes at altitude.         <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;32m╭─\u001b[0m\u001b[1;32m─────────────────────────────────────────\u001b[0m\u001b[1;32m Generated Content with RAG \u001b[0m\u001b[1;32m──────────────────────────────────────────\u001b[0m\u001b[1;32m─╮\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m Yes, mosquitoes in high altitude can expand viruses over large distances. A study intercepted 1,017 female      \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m mosquitoes at altitudes of 120-290 m above ground over Mali and Ghana and screened them for infection with      \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m arboviruses, plasmodia, and filariae. The study found that 3.5% of the mosquitoes were infected with            \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m flaviviruses, and 1.1% were infectious. Additionally, the study identified 19 mosquito-borne pathogens,         \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m including three arboviruses that affect humans (dengue, West Nile, and M’Poko viruses). The study provides      \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m compelling evidence that mosquito-borne pathogens are often spread by windborne mosquitoes at altitude.         \u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n",
+    "\n",
+    "filters = MetadataFilters(\n",
+    "    filters=[\n",
+    "        ExactMatchFilter(key=\"filename\", value=\"nihpp-2024.12.26.630351v1.nxml\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "query_engine = index.as_query_engine(llm=GEN_MODEL, filter=filters, similarity_top_k=3)\n",
+    "result = query_engine.query(query)\n",
+    "\n",
+    "console.print(\n",
+    "    Panel(\n",
+    "        result.response.strip(),\n",
+    "        title=\"Generated Content with RAG\",\n",
+    "        border_style=\"bold green\",\n",
+    "    )\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/custom_convert.py b/docs/examples/custom_convert.py
index a7efa975..49ab739a 100644
--- a/docs/examples/custom_convert.py
+++ b/docs/examples/custom_convert.py
@@ -5,7 +5,11 @@ from pathlib import Path
 
 from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
 from docling.datamodel.base_models import InputFormat
-from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.datamodel.pipeline_options import (
+    AcceleratorDevice,
+    AcceleratorOptions,
+    PdfPipelineOptions,
+)
 from docling.document_converter import DocumentConverter, PdfFormatOption
 from docling.models.ocr_mac_model import OcrMacOptions
 from docling.models.tesseract_ocr_cli_model import TesseractCliOcrOptions
@@ -76,7 +80,7 @@ def main():
     pipeline_options.table_structure_options.do_cell_matching = True
     pipeline_options.ocr_options.lang = ["es"]
     pipeline_options.accelerator_options = AcceleratorOptions(
-        num_threads=4, device=Device.AUTO
+        num_threads=4, device=AcceleratorDevice.AUTO
     )
 
     doc_converter = DocumentConverter(
diff --git a/docs/examples/develop_formula_understanding.py b/docs/examples/develop_formula_understanding.py
new file mode 100644
index 00000000..e2e508c3
--- /dev/null
+++ b/docs/examples/develop_formula_understanding.py
@@ -0,0 +1,88 @@
+import logging
+from pathlib import Path
+from typing import Iterable
+
+from docling_core.types.doc import DocItemLabel, DoclingDocument, NodeItem, TextItem
+
+from docling.datamodel.base_models import InputFormat, ItemAndImageEnrichmentElement
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.models.base_model import BaseItemAndImageEnrichmentModel
+from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
+
+
+class ExampleFormulaUnderstandingPipelineOptions(PdfPipelineOptions):
+    do_formula_understanding: bool = True
+
+
+# A new enrichment model using both the document element and its image as input
+class ExampleFormulaUnderstandingEnrichmentModel(BaseItemAndImageEnrichmentModel):
+    images_scale = 2.6
+
+    def __init__(self, enabled: bool):
+        self.enabled = enabled
+
+    def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
+        return (
+            self.enabled
+            and isinstance(element, TextItem)
+            and element.label == DocItemLabel.FORMULA
+        )
+
+    def __call__(
+        self,
+        doc: DoclingDocument,
+        element_batch: Iterable[ItemAndImageEnrichmentElement],
+    ) -> Iterable[NodeItem]:
+        if not self.enabled:
+            return
+
+        for enrich_element in element_batch:
+            enrich_element.image.show()
+
+            yield enrich_element.item
+
+
+# How the pipeline can be extended.
+class ExampleFormulaUnderstandingPipeline(StandardPdfPipeline):
+
+    def __init__(self, pipeline_options: ExampleFormulaUnderstandingPipelineOptions):
+        super().__init__(pipeline_options)
+        self.pipeline_options: ExampleFormulaUnderstandingPipelineOptions
+
+        self.enrichment_pipe = [
+            ExampleFormulaUnderstandingEnrichmentModel(
+                enabled=self.pipeline_options.do_formula_understanding
+            )
+        ]
+
+        if self.pipeline_options.do_formula_understanding:
+            self.keep_backend = True
+
+    @classmethod
+    def get_default_options(cls) -> ExampleFormulaUnderstandingPipelineOptions:
+        return ExampleFormulaUnderstandingPipelineOptions()
+
+
+# Example main. In the final version, we simply have to set do_formula_understanding to true.
+def main():
+    logging.basicConfig(level=logging.INFO)
+
+    input_doc_path = Path("./tests/data/2203.01017v2.pdf")
+
+    pipeline_options = ExampleFormulaUnderstandingPipelineOptions()
+    pipeline_options.do_formula_understanding = True
+
+    doc_converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(
+                pipeline_cls=ExampleFormulaUnderstandingPipeline,
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+    result = doc_converter.convert(input_doc_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/develop_picture_enrichment.py b/docs/examples/develop_picture_enrichment.py
index 7ad06e4a..81009fe8 100644
--- a/docs/examples/develop_picture_enrichment.py
+++ b/docs/examples/develop_picture_enrichment.py
@@ -22,7 +22,6 @@ class ExamplePictureClassifierPipelineOptions(PdfPipelineOptions):
 
 
 class ExamplePictureClassifierEnrichmentModel(BaseEnrichmentModel):
-
     def __init__(self, enabled: bool):
         self.enabled = enabled
 
@@ -54,7 +53,6 @@ class ExamplePictureClassifierEnrichmentModel(BaseEnrichmentModel):
 
 
 class ExamplePictureClassifierPipeline(StandardPdfPipeline):
-
     def __init__(self, pipeline_options: ExamplePictureClassifierPipelineOptions):
         super().__init__(pipeline_options)
         self.pipeline_options: ExamplePictureClassifierPipeline
diff --git a/docs/examples/inspect_picture_content.py b/docs/examples/inspect_picture_content.py
new file mode 100644
index 00000000..7930acf5
--- /dev/null
+++ b/docs/examples/inspect_picture_content.py
@@ -0,0 +1,29 @@
+from docling_core.types.doc import TextItem
+
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+
+source = "tests/data/amt_handbook_sample.pdf"
+
+pipeline_options = PdfPipelineOptions()
+pipeline_options.images_scale = 2
+pipeline_options.generate_page_images = True
+
+doc_converter = DocumentConverter(
+    format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)}
+)
+
+result = doc_converter.convert(source)
+
+doc = result.document
+
+for picture in doc.pictures:
+    # picture.get_image(doc).show() # display the picture
+    print(picture.caption_text(doc), " contains these elements:")
+
+    for item, level in doc.iterate_items(root=picture, traverse_pictures=True):
+        if isinstance(item, TextItem):
+            print(item.text)
+
+    print("\n")
diff --git a/docs/examples/rag_azuresearch.ipynb b/docs/examples/rag_azuresearch.ipynb
new file mode 100644
index 00000000..dcfd19e3
--- /dev/null
+++ b/docs/examples/rag_azuresearch.ipynb
@@ -0,0 +1,894 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ag9kcX2B_atc"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/DS4SD/docling/blob/main/docs/examples/rag_azuresearch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RAG with Azure AI Search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "| Step               | Tech               | Execution |\n",
+    "| ------------------ | ------------------ | --------- |\n",
+    "| Embedding          | Azure OpenAI       | 🌐 Remote |\n",
+    "| Vector Store       | Azure AI Search    | 🌐 Remote |\n",
+    "| Gen AI  | Azure OpenAI | 🌐 Remote |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## A recipe 🧑‍🍳 🐥 💚\n",
+    "\n",
+    "This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:\n",
+    "- [Docling](https://ds4sd.github.io/docling/) for document parsing and chunking\n",
+    "- [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search/?msockid=0109678bea39665431e37323ebff6723) for vector indexing and retrieval\n",
+    "- [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service?msockid=0109678bea39665431e37323ebff6723) for embeddings and chat completion\n",
+    "\n",
+    "This sample demonstrates how to:\n",
+    "1. Parse a PDF with Docling.\n",
+    "2. Chunk the parsed text.\n",
+    "3. Use Azure OpenAI for embeddings.\n",
+    "4. Index and search in Azure AI Search.\n",
+    "5. Run a retrieval-augmented generation (RAG) query with Azure OpenAI GPT-4o.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If running in a fresh environment (like Google Colab), uncomment and run this single command:\n",
+    "%pip install \"docling~=2.12\" azure-search-documents==11.5.2 azure-identity openai rich torch python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 0: Prerequisites\n",
+    " - **Azure AI Search** resource\n",
+    " - **Azure OpenAI** resource with a deployed embedding and chat completion model (e.g. `text-embedding-3-small` and `gpt-4o`) \n",
+    " - **Docling 2.12+** (installs `docling_core` automatically)  Docling installed (Python 3.8+ environment)\n",
+    "\n",
+    "- A **GPU-enabled environment** is preferred for faster parsing. Docling 2.12 automatically detects GPU if present.\n",
+    "  - If you only have CPU, parsing large PDFs can be slower.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "\n",
+    "def _get_env(key, default=None):\n",
+    "    try:\n",
+    "        from google.colab import userdata\n",
+    "\n",
+    "        try:\n",
+    "            return userdata.get(key)\n",
+    "        except userdata.SecretNotFoundError:\n",
+    "            pass\n",
+    "    except ImportError:\n",
+    "        pass\n",
+    "    return os.getenv(key, default)\n",
+    "\n",
+    "\n",
+    "AZURE_SEARCH_ENDPOINT = _get_env(\"AZURE_SEARCH_ENDPOINT\")\n",
+    "AZURE_SEARCH_KEY = _get_env(\"AZURE_SEARCH_KEY\")  # Ensure this is your Admin Key\n",
+    "AZURE_SEARCH_INDEX_NAME = _get_env(\"AZURE_SEARCH_INDEX_NAME\", \"docling-rag-sample\")\n",
+    "AZURE_OPENAI_ENDPOINT = _get_env(\"AZURE_OPENAI_ENDPOINT\")\n",
+    "AZURE_OPENAI_API_KEY = _get_env(\"AZURE_OPENAI_API_KEY\")\n",
+    "AZURE_OPENAI_API_VERSION = _get_env(\"AZURE_OPENAI_API_VERSION\", \"2024-10-21\")\n",
+    "AZURE_OPENAI_CHAT_MODEL = _get_env(\n",
+    "    \"AZURE_OPENAI_CHAT_MODEL\"\n",
+    ")  # Using a deployed model named \"gpt-4o\"\n",
+    "AZURE_OPENAI_EMBEDDINGS = _get_env(\n",
+    "    \"AZURE_OPENAI_EMBEDDINGS\", \"text-embedding-3-small\"\n",
+    ")  # Using a deployed model named \"text-embeddings-3-small\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 1: Parse the PDF with Docling\n",
+    "\n",
+    "We’ll parse the **Microsoft GraphRAG Research Paper** (~15 pages). Parsing should be relatively quick, even on CPU, but it will be faster on a GPU or MPS device if available.\n",
+    "\n",
+    "*(If you prefer a different document, simply provide a different URL or local file path.)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Parsing a ~</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">15</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">-page PDF. The process should be relatively quick, even on CPU...</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;33mParsing a ~\u001b[0m\u001b[1;33m15\u001b[0m\u001b[1;33m-page PDF. The process should be relatively quick, even on CPU\u001b[0m\u001b[1;33m...\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────────────── Docling Markdown Preview ────────────────────────────────────────────╮\n",
+       "│ ## From Local to Global: A Graph RAG Approach to Query-Focused Summarization                                    │\n",
+       "│                                                                                                                 │\n",
+       "│ Darren Edge 1†                                                                                                  │\n",
+       "│                                                                                                                 │\n",
+       "│ Ha Trinh 1†                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ Newman Cheng 2                                                                                                  │\n",
+       "│                                                                                                                 │\n",
+       "│ Joshua Bradley 2                                                                                                │\n",
+       "│                                                                                                                 │\n",
+       "│ Alex Chao 3                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ Apurva Mody 3                                                                                                   │\n",
+       "│                                                                                                                 │\n",
+       "│ Steven Truitt 2                                                                                                 │\n",
+       "│                                                                                                                 │\n",
+       "│ ## Jonathan Larson 1                                                                                            │\n",
+       "│                                                                                                                 │\n",
+       "│ 1 Microsoft Research 2 Microsoft Strategic Missions and Technologies 3 Microsoft Office of the CTO              │\n",
+       "│                                                                                                                 │\n",
+       "│ { daedge,trinhha,newmancheng,joshbradley,achao,moapurva,steventruitt,jolarso } @microsoft.com                   │\n",
+       "│                                                                                                                 │\n",
+       "│ † These authors contributed equally to this work                                                                │\n",
+       "│                                                                                                                 │\n",
+       "│ ## Abstract                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ The use of retrieval-augmented gen...                                                                           │\n",
+       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "╭─────────────────────────────────────────── Docling Markdown Preview ────────────────────────────────────────────╮\n",
+       "│ ## From Local to Global: A Graph RAG Approach to Query-Focused Summarization                                    │\n",
+       "│                                                                                                                 │\n",
+       "│ Darren Edge 1†                                                                                                  │\n",
+       "│                                                                                                                 │\n",
+       "│ Ha Trinh 1†                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ Newman Cheng 2                                                                                                  │\n",
+       "│                                                                                                                 │\n",
+       "│ Joshua Bradley 2                                                                                                │\n",
+       "│                                                                                                                 │\n",
+       "│ Alex Chao 3                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ Apurva Mody 3                                                                                                   │\n",
+       "│                                                                                                                 │\n",
+       "│ Steven Truitt 2                                                                                                 │\n",
+       "│                                                                                                                 │\n",
+       "│ ## Jonathan Larson 1                                                                                            │\n",
+       "│                                                                                                                 │\n",
+       "│ 1 Microsoft Research 2 Microsoft Strategic Missions and Technologies 3 Microsoft Office of the CTO              │\n",
+       "│                                                                                                                 │\n",
+       "│ { daedge,trinhha,newmancheng,joshbradley,achao,moapurva,steventruitt,jolarso } @microsoft.com                   │\n",
+       "│                                                                                                                 │\n",
+       "│ † These authors contributed equally to this work                                                                │\n",
+       "│                                                                                                                 │\n",
+       "│ ## Abstract                                                                                                     │\n",
+       "│                                                                                                                 │\n",
+       "│ The use of retrieval-augmented gen...                                                                           │\n",
+       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from rich.console import Console\n",
+    "from rich.panel import Panel\n",
+    "\n",
+    "from docling.document_converter import DocumentConverter\n",
+    "\n",
+    "console = Console()\n",
+    "\n",
+    "# This URL points to the Microsoft GraphRAG Research Paper (arXiv: 2404.16130), ~15 pages\n",
+    "source_url = \"https://arxiv.org/pdf/2404.16130\"\n",
+    "\n",
+    "console.print(\n",
+    "    \"[bold yellow]Parsing a ~15-page PDF. The process should be relatively quick, even on CPU...[/bold yellow]\"\n",
+    ")\n",
+    "converter = DocumentConverter()\n",
+    "result = converter.convert(source_url)\n",
+    "\n",
+    "# Optional: preview the parsed Markdown\n",
+    "md_preview = result.document.export_to_markdown()\n",
+    "console.print(Panel(md_preview[:500] + \"...\", title=\"Docling Markdown Preview\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 2: Hierarchical Chunking\n",
+    "We convert the `Document` into smaller chunks for embedding and indexing. The built-in `HierarchicalChunker` preserves structure. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Total chunks from PDF: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">106</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Total chunks from PDF: \u001b[1;36m106\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from docling.chunking import HierarchicalChunker\n",
+    "\n",
+    "chunker = HierarchicalChunker()\n",
+    "doc_chunks = list(chunker.chunk(result.document))\n",
+    "\n",
+    "all_chunks = []\n",
+    "for idx, c in enumerate(doc_chunks):\n",
+    "    chunk_text = c.text\n",
+    "    all_chunks.append((f\"chunk_{idx}\", chunk_text))\n",
+    "\n",
+    "console.print(f\"Total chunks from PDF: {len(all_chunks)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 3: Create Azure AI Search Index and Push Chunk Embeddings\n",
+    "We’ll define a vector index in Azure AI Search, then embed each chunk using Azure OpenAI and upload in batches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Index <span style=\"color: #008000; text-decoration-color: #008000\">'docling-rag-sample-2'</span> created.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Index \u001b[32m'docling-rag-sample-2'\u001b[0m created.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from azure.core.credentials import AzureKeyCredential\n",
+    "from azure.search.documents.indexes import SearchIndexClient\n",
+    "from azure.search.documents.indexes.models import (\n",
+    "    AzureOpenAIVectorizer,\n",
+    "    AzureOpenAIVectorizerParameters,\n",
+    "    HnswAlgorithmConfiguration,\n",
+    "    SearchableField,\n",
+    "    SearchField,\n",
+    "    SearchFieldDataType,\n",
+    "    SearchIndex,\n",
+    "    SimpleField,\n",
+    "    VectorSearch,\n",
+    "    VectorSearchProfile,\n",
+    ")\n",
+    "from rich.console import Console\n",
+    "\n",
+    "console = Console()\n",
+    "\n",
+    "VECTOR_DIM = 1536  # Adjust based on your chosen embeddings model\n",
+    "\n",
+    "index_client = SearchIndexClient(\n",
+    "    AZURE_SEARCH_ENDPOINT, AzureKeyCredential(AZURE_SEARCH_KEY)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def create_search_index(index_name: str):\n",
+    "    # Define fields\n",
+    "    fields = [\n",
+    "        SimpleField(name=\"chunk_id\", type=SearchFieldDataType.String, key=True),\n",
+    "        SearchableField(name=\"content\", type=SearchFieldDataType.String),\n",
+    "        SearchField(\n",
+    "            name=\"content_vector\",\n",
+    "            type=SearchFieldDataType.Collection(SearchFieldDataType.Single),\n",
+    "            searchable=True,\n",
+    "            filterable=False,\n",
+    "            sortable=False,\n",
+    "            facetable=False,\n",
+    "            vector_search_dimensions=VECTOR_DIM,\n",
+    "            vector_search_profile_name=\"default\",\n",
+    "        ),\n",
+    "    ]\n",
+    "    # Vector search config with an AzureOpenAIVectorizer\n",
+    "    vector_search = VectorSearch(\n",
+    "        algorithms=[HnswAlgorithmConfiguration(name=\"default\")],\n",
+    "        profiles=[\n",
+    "            VectorSearchProfile(\n",
+    "                name=\"default\",\n",
+    "                algorithm_configuration_name=\"default\",\n",
+    "                vectorizer_name=\"default\",\n",
+    "            )\n",
+    "        ],\n",
+    "        vectorizers=[\n",
+    "            AzureOpenAIVectorizer(\n",
+    "                vectorizer_name=\"default\",\n",
+    "                parameters=AzureOpenAIVectorizerParameters(\n",
+    "                    resource_url=AZURE_OPENAI_ENDPOINT,\n",
+    "                    deployment_name=AZURE_OPENAI_EMBEDDINGS,\n",
+    "                    model_name=\"text-embedding-3-small\",\n",
+    "                    api_key=AZURE_OPENAI_API_KEY,\n",
+    "                ),\n",
+    "            )\n",
+    "        ],\n",
+    "    )\n",
+    "\n",
+    "    # Create or update the index\n",
+    "    new_index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search)\n",
+    "    try:\n",
+    "        index_client.delete_index(index_name)\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    index_client.create_or_update_index(new_index)\n",
+    "    console.print(f\"Index '{index_name}' created.\")\n",
+    "\n",
+    "\n",
+    "create_search_index(AZURE_SEARCH_INDEX_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate Embeddings and Upload to Azure AI Search\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Uploaded batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -&gt; <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span>; all_succeeded: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, first_doc_status_code: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">201</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Uploaded batch \u001b[1;36m0\u001b[0m -> \u001b[1;36m50\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Uploaded batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span> -&gt; <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>; all_succeeded: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, first_doc_status_code: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">201</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Uploaded batch \u001b[1;36m50\u001b[0m -> \u001b[1;36m100\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Uploaded batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span> -&gt; <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">106</span>; all_succeeded: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, first_doc_status_code: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">201</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "Uploaded batch \u001b[1;36m100\u001b[0m -> \u001b[1;36m106\u001b[0m; all_succeeded: \u001b[3;92mTrue\u001b[0m, first_doc_status_code: \u001b[1;36m201\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">All chunks uploaded to Azure Search.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "All chunks uploaded to Azure Search.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from azure.search.documents import SearchClient\n",
+    "from openai import AzureOpenAI\n",
+    "\n",
+    "search_client = SearchClient(\n",
+    "    AZURE_SEARCH_ENDPOINT, AZURE_SEARCH_INDEX_NAME, AzureKeyCredential(AZURE_SEARCH_KEY)\n",
+    ")\n",
+    "openai_client = AzureOpenAI(\n",
+    "    api_key=AZURE_OPENAI_API_KEY,\n",
+    "    api_version=AZURE_OPENAI_API_VERSION,\n",
+    "    azure_endpoint=AZURE_OPENAI_ENDPOINT,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def embed_text(text: str):\n",
+    "    \"\"\"\n",
+    "    Helper to generate embeddings with Azure OpenAI.\n",
+    "    \"\"\"\n",
+    "    response = openai_client.embeddings.create(\n",
+    "        input=text, model=AZURE_OPENAI_EMBEDDINGS\n",
+    "    )\n",
+    "    return response.data[0].embedding\n",
+    "\n",
+    "\n",
+    "upload_docs = []\n",
+    "for chunk_id, chunk_text in all_chunks:\n",
+    "    embedding_vector = embed_text(chunk_text)\n",
+    "    upload_docs.append(\n",
+    "        {\n",
+    "            \"chunk_id\": chunk_id,\n",
+    "            \"content\": chunk_text,\n",
+    "            \"content_vector\": embedding_vector,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "BATCH_SIZE = 50\n",
+    "for i in range(0, len(upload_docs), BATCH_SIZE):\n",
+    "    subset = upload_docs[i : i + BATCH_SIZE]\n",
+    "    resp = search_client.upload_documents(documents=subset)\n",
+    "\n",
+    "    all_succeeded = all(r.succeeded for r in resp)\n",
+    "    console.print(\n",
+    "        f\"Uploaded batch {i} -> {i+len(subset)}; all_succeeded: {all_succeeded}, \"\n",
+    "        f\"first_doc_status_code: {resp[0].status_code}\"\n",
+    "    )\n",
+    "\n",
+    "console.print(\"All chunks uploaded to Azure Search.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part 4: Perform RAG over PDF\n",
+    "Combine retrieval from Azure AI Search with Azure OpenAI Chat Completions (aka. grounding your LLM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">╭──────────────────────────────────────────────────</span> RAG Prompt <span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">───────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ You are an AI assistant helping answering questions about Microsoft GraphRAG.                                   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Use ONLY the text below to answer the user's question.                                                          │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ If the answer isn't in the text, say you don't know.                                                            │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Context:                                                                                                        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Community summaries vs. source texts. When comparing community summaries to source texts using Graph RAG,       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ community summaries generally provided a small but consistent improvement in answer comprehensiveness and       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ diversity, except for root-level summaries. Intermediate-level summaries in the Podcast dataset and low-level   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ community summaries in the News dataset achieved comprehensiveness win rates of 57% and 64%, respectively.      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Diversity win rates were 57% for Podcast intermediate-level summaries and 60% for News low-level community      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ summaries. Table 3 also illustrates the scalability advantages of Graph RAG compared to source text             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ summarization: for low-level community summaries ( C3 ), Graph RAG required 26-33% fewer context tokens, while  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ for root-level community summaries ( C0 ), it required over 97% fewer tokens. For a modest drop in performance  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ compared with other global methods, root-level Graph RAG offers a highly efficient method for the iterative     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ question answering that characterizes sensemaking activity, while retaining advantages in comprehensiveness     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ (72% win rate) and diversity (62% win rate) over na¨ıve RAG.                                                    │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ We have presented a global approach to Graph RAG, combining knowledge graph generation, retrieval-augmented     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ generation (RAG), and query-focused summarization (QFS) to support human sensemaking over entire text corpora.  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Initial evaluations show substantial improvements over a na¨ıve RAG baseline for both the comprehensiveness and │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ diversity of answers, as well as favorable comparisons to a global but graph-free approach using map-reduce     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ source text summarization. For situations requiring many global queries over the same dataset, summaries of     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ root-level communities in the entity-based graph index provide a data index that is both superior to na¨ıve RAG │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ and achieves competitive performance to other global methods at a fraction of the token cost.                   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Trade-offs of building a graph index . We consistently observed Graph RAG achieve the best headto-head results  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ against other methods, but in many cases the graph-free approach to global summarization of source texts        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ performed competitively. The real-world decision about whether to invest in building a graph index depends on   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ multiple factors, including the compute budget, expected number of lifetime queries per dataset, and value      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ obtained from other aspects of the graph index (including the generic community summaries and the use of other  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ graph-related RAG approaches).                                                                                  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Future work . The graph index, rich text annotations, and hierarchical community structure supporting the       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ current Graph RAG approach offer many possibilities for refinement and adaptation. This includes RAG approaches │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ that operate in a more local manner, via embedding-based matching of user queries and graph annotations, as     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ well as the possibility of hybrid RAG schemes that combine embedding-based matching against community reports   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ before employing our map-reduce summarization mechanisms. This 'roll-up' operation could also be extended       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ across more levels of the community hierarchy, as well as implemented as a more exploratory 'drill down'        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ mechanism that follows the information scent contained in higher-level community summaries.                     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Advanced RAG systems include pre-retrieval, retrieval, post-retrieval strategies designed to overcome the       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ drawbacks of Na¨ıve RAG, while Modular RAG systems include patterns for iterative and dynamic cycles of         │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ interleaved retrieval and generation (Gao et al., 2023). Our implementation of Graph RAG incorporates multiple  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ concepts related to other systems. For example, our community summaries are a kind of self-memory (Selfmem,     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Cheng et al., 2024) for generation-augmented retrieval (GAR, Mao et al., 2020) that facilitates future          │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ generation cycles, while our parallel generation of community answers from these summaries is a kind of         │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ iterative (Iter-RetGen, Shao et al., 2023) or federated (FeB4RAG, Wang et al., 2024) retrieval-generation       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ strategy. Other systems have also combined these concepts for multi-document summarization (CAiRE-COVID, Su et  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ al., 2020) and multi-hop question answering (ITRG, Feng et al., 2023; IR-CoT, Trivedi et al., 2022; DSP,        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Khattab et al., 2022). Our use of a hierarchical index and summarization also bears resemblance to further      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ approaches, such as generating a hierarchical index of text chunks by clustering the vectors of text embeddings │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ (RAPTOR, Sarthi et al., 2024) or generating a 'tree of clarifications' to answer multiple interpretations of    │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ambiguous questions (Kim et al., 2023). However, none of these iterative or hierarchical approaches use the     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ kind of self-generated graph index that enables Graph RAG.                                                      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ source enables large language models (LLMs) to answer questions over private and/or previously unseen document  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ collections. However, RAG fails on global questions directed at an entire text corpus, such as 'What are the    │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ main themes in the dataset?', since this is inherently a queryfocused summarization (QFS) task, rather than an  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ explicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ typical RAGsystems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ question answering over private text corpora that scales with both the generality of user questions and the     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ quantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ stages: first to derive an entity knowledge graph from the source documents, then to pregenerate community      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ summaries for all groups of closely-related entities. Given a question, each community summary is used to       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ generate a partial response, before all partial responses are again summarized in a final response to the user. │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ leads to substantial improvements over a na¨ıve RAG baseline for both the comprehensiveness and diversity of    │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ generated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ forthcoming at https://aka . ms/graphrag .                                                                      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Given the multi-stage nature of our Graph RAG mechanism, the multiple conditions we wanted to compare, and the  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ lack of gold standard answers to our activity-based sensemaking questions, we decided to adopt a head-to-head   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ comparison approach using an LLM evaluator. We selected three target metrics capturing qualities that are       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ desirable for sensemaking activities, as well as a control metric (directness) used as a indicator of validity. │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Since directness is effectively in opposition to comprehensiveness and diversity, we would not expect any       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ method to win across all four metrics.                                                                          │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Figure 1: Graph RAG pipeline using an LLM-derived graph index of source document text. This index spans nodes   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ (e.g., entities), edges (e.g., relationships), and covariates (e.g., claims) that have been detected,           │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ extracted, and summarized by LLM prompts tailored to the domain of the dataset. Community detection (e.g.,      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Leiden, Traag et al., 2019) is used to partition the graph index into groups of elements (nodes, edges,         │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ covariates) that the LLM can summarize in parallel at both indexing time and query time. The 'global answer' to │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ a given query is produced using a final round of query-focused summarization over all community summaries       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ reporting relevance to that query.                                                                              │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Retrieval-augmented generation (RAG, Lewis et al., 2020) is an established approach to answering user questions │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ over entire datasets, but it is designed for situations where these answers are contained locally within        │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ regions of text whose retrieval provides sufficient grounding for the generation task. Instead, a more          │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ appropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ abstractive summarization that generates natural language summaries and not just concatenated excerpts (Baumel  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ et al., 2018; Laskar et al., 2020; Yao et al., 2017) . In recent years, however, such distinctions between      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ summarization tasks that are abstractive versus extractive, generic versus query-focused, and single-document   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ versus multi-document, have become less relevant. While early applications of the transformer architecture      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ showed substantial improvements on the state-of-the-art for all such summarization tasks (Goodwin et al., 2020; │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Laskar et al., 2022; Liu and Lapata, 2019), these tasks are now trivialized by modern LLMs, including the GPT   │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ (Achiam et al., 2023; Brown et al., 2020), Llama (Touvron et al., 2023), and Gemini (Anil et al., 2023) series, │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ all of which can use in-context learning to summarize any content provided in their context window.             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ ---                                                                                                             │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ community descriptions provide complete coverage of the underlying graph index and the input documents it       │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ represents. Query-focused summarization of an entire corpus is then made possible using a map-reduce approach:  │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ first using each community summary to answer the query independently and in parallel, then summarizing all      │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ relevant partial answers into a final global answer.                                                            │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Question: What are the main advantages of using the Graph RAG approach for query-focused summarization compared │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ to traditional RAG methods?                                                                                     │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│ Answer:                                                                                                         │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;31m╭─\u001b[0m\u001b[1;31m─────────────────────────────────────────────────\u001b[0m RAG Prompt \u001b[1;31m──────────────────────────────────────────────────\u001b[0m\u001b[1;31m─╮\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m                                                                                                               \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mYou are an AI assistant helping answering questions about Microsoft GraphRAG.\u001b[0m\u001b[1;31m                                  \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mUse ONLY the text below to answer the user's question.\u001b[0m\u001b[1;31m                                                         \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mIf the answer isn't in the text, say you don't know.\u001b[0m\u001b[1;31m                                                           \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m                                                                                                               \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mContext:\u001b[0m\u001b[1;31m                                                                                                       \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mCommunity summaries vs. source texts. When comparing community summaries to source texts using Graph RAG, \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity summaries generally provided a small but consistent improvement in answer comprehensiveness and \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdiversity, except for root-level summaries. Intermediate-level summaries in the Podcast dataset and low-level \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity summaries in the News dataset achieved comprehensiveness win rates of 57% and 64%, respectively. \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mDiversity win rates were 57% for Podcast intermediate-level summaries and 60% for News low-level community \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummaries. Table 3 also illustrates the scalability advantages of Graph RAG compared to source text \u001b[0m\u001b[1;31m           \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummarization: for low-level community summaries ( C3 ), Graph RAG required 26-33% fewer context tokens, while \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mfor root-level community summaries ( C0 ), it required over 97% fewer tokens. For a modest drop in performance \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcompared with other global methods, root-level Graph RAG offers a highly efficient method for the iterative \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquestion answering that characterizes sensemaking activity, while retaining advantages in comprehensiveness \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(72% win rate) and diversity (62% win rate) over na¨ıve RAG.\u001b[0m\u001b[1;31m                                                   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mWe have presented a global approach to Graph RAG, combining knowledge graph generation, retrieval-augmented \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgeneration (RAG), and query-focused summarization (QFS) to support human sensemaking over entire text corpora. \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mInitial evaluations show substantial improvements over a na¨ıve RAG baseline for both the comprehensiveness and\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdiversity of answers, as well as favorable comparisons to a global but graph-free approach using map-reduce \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msource text summarization. For situations requiring many global queries over the same dataset, summaries of \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mroot-level communities in the entity-based graph index provide a data index that is both superior to na¨ıve RAG\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mand achieves competitive performance to other global methods at a fraction of the token cost.\u001b[0m\u001b[1;31m                  \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mTrade-offs of building a graph index . We consistently observed Graph RAG achieve the best headto-head results \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31magainst other methods, but in many cases the graph-free approach to global summarization of source texts \u001b[0m\u001b[1;31m      \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mperformed competitively. The real-world decision about whether to invest in building a graph index depends on \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmultiple factors, including the compute budget, expected number of lifetime queries per dataset, and value \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mobtained from other aspects of the graph index (including the generic community summaries and the use of other \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgraph-related RAG approaches).\u001b[0m\u001b[1;31m                                                                                 \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFuture work . The graph index, rich text annotations, and hierarchical community structure supporting the \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcurrent Graph RAG approach offer many possibilities for refinement and adaptation. This includes RAG approaches\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mthat operate in a more local manner, via embedding-based matching of user queries and graph annotations, as \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mwell as the possibility of hybrid RAG schemes that combine embedding-based matching against community reports \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mbefore employing our map-reduce summarization mechanisms. This 'roll-up' operation could also be extended \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31macross more levels of the community hierarchy, as well as implemented as a more exploratory 'drill down' \u001b[0m\u001b[1;31m      \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmechanism that follows the information scent contained in higher-level community summaries.\u001b[0m\u001b[1;31m                    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mAdvanced RAG systems include pre-retrieval, retrieval, post-retrieval strategies designed to overcome the \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdrawbacks of Na¨ıve RAG, while Modular RAG systems include patterns for iterative and dynamic cycles of \u001b[0m\u001b[1;31m       \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31minterleaved retrieval and generation (Gao et al., 2023). Our implementation of Graph RAG incorporates multiple \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mconcepts related to other systems. For example, our community summaries are a kind of self-memory (Selfmem, \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mCheng et al., 2024) for generation-augmented retrieval (GAR, Mao et al., 2020) that facilitates future \u001b[0m\u001b[1;31m        \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgeneration cycles, while our parallel generation of community answers from these summaries is a kind of \u001b[0m\u001b[1;31m       \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31miterative (Iter-RetGen, Shao et al., 2023) or federated (FeB4RAG, Wang et al., 2024) retrieval-generation \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mstrategy. Other systems have also combined these concepts for multi-document summarization (CAiRE-COVID, Su et \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mal., 2020) and multi-hop question answering (ITRG, Feng et al., 2023; IR-CoT, Trivedi et al., 2022; DSP, \u001b[0m\u001b[1;31m      \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mKhattab et al., 2022). Our use of a hierarchical index and summarization also bears resemblance to further \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mapproaches, such as generating a hierarchical index of text chunks by clustering the vectors of text embeddings\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(RAPTOR, Sarthi et al., 2024) or generating a 'tree of clarifications' to answer multiple interpretations of \u001b[0m\u001b[1;31m  \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mambiguous questions (Kim et al., 2023). However, none of these iterative or hierarchical approaches use the \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mkind of self-generated graph index that enables Graph RAG.\u001b[0m\u001b[1;31m                                                     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mThe use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msource enables large language models (LLMs) to answer questions over private and/or previously unseen document \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcollections. However, RAG fails on global questions directed at an entire text corpus, such as 'What are the \u001b[0m\u001b[1;31m  \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmain themes in the dataset?', since this is inherently a queryfocused summarization (QFS) task, rather than an \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mexplicit retrieval task. Prior QFS methods, meanwhile, fail to scale to the quantities of text indexed by \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mtypical RAGsystems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquestion answering over private text corpora that scales with both the generality of user questions and the \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mquantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two \u001b[0m\u001b[1;31m      \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mstages: first to derive an entity knowledge graph from the source documents, then to pregenerate community \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummaries for all groups of closely-related entities. Given a question, each community summary is used to \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgenerate a partial response, before all partial responses are again summarized in a final response to the user.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFor a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mleads to substantial improvements over a na¨ıve RAG baseline for both the comprehensiveness and diversity of \u001b[0m\u001b[1;31m  \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mgenerated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mforthcoming at https://aka . ms/graphrag .\u001b[0m\u001b[1;31m                                                                     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mGiven the multi-stage nature of our Graph RAG mechanism, the multiple conditions we wanted to compare, and the \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mlack of gold standard answers to our activity-based sensemaking questions, we decided to adopt a head-to-head \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcomparison approach using an LLM evaluator. We selected three target metrics capturing qualities that are \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mdesirable for sensemaking activities, as well as a control metric (directness) used as a indicator of validity.\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mSince directness is effectively in opposition to comprehensiveness and diversity, we would not expect any \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mmethod to win across all four metrics.\u001b[0m\u001b[1;31m                                                                         \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mFigure 1: Graph RAG pipeline using an LLM-derived graph index of source document text. This index spans nodes \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(e.g., entities), edges (e.g., relationships), and covariates (e.g., claims) that have been detected, \u001b[0m\u001b[1;31m         \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mextracted, and summarized by LLM prompts tailored to the domain of the dataset. Community detection (e.g., \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mLeiden, Traag et al., 2019) is used to partition the graph index into groups of elements (nodes, edges, \u001b[0m\u001b[1;31m       \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcovariates) that the LLM can summarize in parallel at both indexing time and query time. The 'global answer' to\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31ma given query is produced using a final round of query-focused summarization over all community summaries \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mreporting relevance to that query.\u001b[0m\u001b[1;31m                                                                             \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mRetrieval-augmented generation (RAG, Lewis et al., 2020) is an established approach to answering user questions\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mover entire datasets, but it is designed for situations where these answers are contained locally within \u001b[0m\u001b[1;31m      \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mregions of text whose retrieval provides sufficient grounding for the generation task. Instead, a more \u001b[0m\u001b[1;31m        \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mappropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused \u001b[0m\u001b[1;31m   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mabstractive summarization that generates natural language summaries and not just concatenated excerpts (Baumel \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31met al., 2018; Laskar et al., 2020; Yao et al., 2017) . In recent years, however, such distinctions between \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31msummarization tasks that are abstractive versus extractive, generic versus query-focused, and single-document \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mversus multi-document, have become less relevant. While early applications of the transformer architecture \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mshowed substantial improvements on the state-of-the-art for all such summarization tasks (Goodwin et al., 2020;\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mLaskar et al., 2022; Liu and Lapata, 2019), these tasks are now trivialized by modern LLMs, including the GPT \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(Achiam et al., 2023; Brown et al., 2020), Llama (Touvron et al., 2023), and Gemini (Anil et al., 2023) series,\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mall of which can use in-context learning to summarize any content provided in their context window.\u001b[0m\u001b[1;31m            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m---\u001b[0m\u001b[1;31m                                                                                                            \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mcommunity descriptions provide complete coverage of the underlying graph index and the input documents it \u001b[0m\u001b[1;31m     \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mrepresents. Query-focused summarization of an entire corpus is then made possible using a map-reduce approach: \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mfirst using each community summary to answer the query independently and in parallel, then summarizing all \u001b[0m\u001b[1;31m    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mrelevant partial answers into a final global answer.\u001b[0m\u001b[1;31m                                                           \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m                                                                                                               \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mQuestion: What are the main advantages of using the Graph RAG approach for query-focused summarization compared\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mto traditional RAG methods?\u001b[0m\u001b[1;31m                                                                                    \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31mAnswer:\u001b[0m\u001b[1;31m                                                                                                        \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m│\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m                                                                                                               \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m│\u001b[0m\n",
+       "\u001b[1;31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╭─────────────────────────────────────────────────</span> RAG Response <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">──────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ The main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ methods include:                                                                                                │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 1. **Improved Comprehensiveness and Diversity**: Graph RAG shows substantial improvements over a naïve RAG      │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ baseline in terms of the comprehensiveness and diversity of answers. This is particularly beneficial for global │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ sensemaking questions over large datasets.                                                                      │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 2. **Scalability**: Graph RAG provides scalability advantages, achieving efficient summarization with           │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ significantly fewer context tokens required. For instance, it requires 26-33% fewer tokens for low-level        │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ community summaries and over 97% fewer tokens for root-level summaries compared to source text summarization.   │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 3. **Efficiency in Iterative Question Answering**: Root-level Graph RAG offers a highly efficient method for    │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ iterative question answering, which is crucial for sensemaking activities, with only a modest drop in           │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ performance compared to other global methods.                                                                   │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 4. **Global Query Handling**: It supports handling global queries effectively, as it combines knowledge graph   │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ generation, retrieval-augmented generation, and query-focused summarization, making it suitable for sensemaking │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ over entire text corpora.                                                                                       │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 5. **Hierarchical Indexing and Summarization**: The use of a hierarchical index and summarization allows for    │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ efficient processing and summarizing of community summaries into a final global answer, facilitating a          │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ comprehensive coverage of the underlying graph index and input documents.                                       │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│                                                                                                                 │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ 6. **Reduced Token Cost**: For situations requiring many global queries over the same dataset, Graph RAG        │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">│ achieves competitive performance to other global methods at a fraction of the token cost.                       │</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;32m╭─\u001b[0m\u001b[1;32m────────────────────────────────────────────────\u001b[0m RAG Response \u001b[1;32m─────────────────────────────────────────────────\u001b[0m\u001b[1;32m─╮\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mThe main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mmethods include:\u001b[0m\u001b[1;32m                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m1. **Improved Comprehensiveness and Diversity**: Graph RAG shows substantial improvements over a naïve RAG \u001b[0m\u001b[1;32m    \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mbaseline in terms of the comprehensiveness and diversity of answers. This is particularly beneficial for global\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32msensemaking questions over large datasets.\u001b[0m\u001b[1;32m                                                                     \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m2. **Scalability**: Graph RAG provides scalability advantages, achieving efficient summarization with \u001b[0m\u001b[1;32m         \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32msignificantly fewer context tokens required. For instance, it requires 26-33% fewer tokens for low-level \u001b[0m\u001b[1;32m      \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mcommunity summaries and over 97% fewer tokens for root-level summaries compared to source text summarization.\u001b[0m\u001b[1;32m  \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m3. **Efficiency in Iterative Question Answering**: Root-level Graph RAG offers a highly efficient method for \u001b[0m\u001b[1;32m  \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32miterative question answering, which is crucial for sensemaking activities, with only a modest drop in \u001b[0m\u001b[1;32m         \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mperformance compared to other global methods.\u001b[0m\u001b[1;32m                                                                  \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m4. **Global Query Handling**: It supports handling global queries effectively, as it combines knowledge graph \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mgeneration, retrieval-augmented generation, and query-focused summarization, making it suitable for sensemaking\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mover entire text corpora.\u001b[0m\u001b[1;32m                                                                                      \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m5. **Hierarchical Indexing and Summarization**: The use of a hierarchical index and summarization allows for \u001b[0m\u001b[1;32m  \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mefficient processing and summarizing of community summaries into a final global answer, facilitating a \u001b[0m\u001b[1;32m        \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32mcomprehensive coverage of the underlying graph index and input documents.\u001b[0m\u001b[1;32m                                      \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m                                                                                                               \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m6. **Reduced Token Cost**: For situations requiring many global queries over the same dataset, Graph RAG \u001b[0m\u001b[1;32m      \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m│\u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32machieves competitive performance to other global methods at a fraction of the token cost.\u001b[0m\u001b[1;32m                      \u001b[0m\u001b[1;32m \u001b[0m\u001b[1;32m│\u001b[0m\n",
+       "\u001b[1;32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from azure.search.documents.models import VectorizableTextQuery\n",
+    "\n",
+    "\n",
+    "def generate_chat_response(prompt: str, system_message: str = None):\n",
+    "    \"\"\"\n",
+    "    Generates a single-turn chat response using Azure OpenAI Chat.\n",
+    "    If you need multi-turn conversation or follow-up queries, you'll have to\n",
+    "    maintain the messages list externally.\n",
+    "    \"\"\"\n",
+    "    messages = []\n",
+    "    if system_message:\n",
+    "        messages.append({\"role\": \"system\", \"content\": system_message})\n",
+    "    messages.append({\"role\": \"user\", \"content\": prompt})\n",
+    "\n",
+    "    completion = openai_client.chat.completions.create(\n",
+    "        model=AZURE_OPENAI_CHAT_MODEL, messages=messages, temperature=0.7\n",
+    "    )\n",
+    "    return completion.choices[0].message.content\n",
+    "\n",
+    "\n",
+    "user_query = \"What are the main advantages of using the Graph RAG approach for query-focused summarization compared to traditional RAG methods?\"\n",
+    "user_embed = embed_text(user_query)\n",
+    "\n",
+    "vector_query = VectorizableTextQuery(\n",
+    "    text=user_query,  # passing in text for a hybrid search\n",
+    "    k_nearest_neighbors=5,\n",
+    "    fields=\"content_vector\",\n",
+    ")\n",
+    "\n",
+    "search_results = search_client.search(\n",
+    "    search_text=user_query, vector_queries=[vector_query], select=[\"content\"], top=10\n",
+    ")\n",
+    "\n",
+    "retrieved_chunks = []\n",
+    "for result in search_results:\n",
+    "    snippet = result[\"content\"]\n",
+    "    retrieved_chunks.append(snippet)\n",
+    "\n",
+    "context_str = \"\\n---\\n\".join(retrieved_chunks)\n",
+    "rag_prompt = f\"\"\"\n",
+    "You are an AI assistant helping answering questions about Microsoft GraphRAG.\n",
+    "Use ONLY the text below to answer the user's question.\n",
+    "If the answer isn't in the text, say you don't know.\n",
+    "\n",
+    "Context:\n",
+    "{context_str}\n",
+    "\n",
+    "Question: {user_query}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "\n",
+    "final_answer = generate_chat_response(rag_prompt)\n",
+    "\n",
+    "console.print(Panel(rag_prompt, title=\"RAG Prompt\", style=\"bold red\"))\n",
+    "console.print(Panel(final_answer, title=\"RAG Response\", style=\"bold green\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docs/examples/tesseract_lang_detection.py b/docs/examples/tesseract_lang_detection.py
new file mode 100644
index 00000000..b75e4707
--- /dev/null
+++ b/docs/examples/tesseract_lang_detection.py
@@ -0,0 +1,37 @@
+from pathlib import Path
+
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import (
+    PdfPipelineOptions,
+    TesseractCliOcrOptions,
+    TesseractOcrOptions,
+)
+from docling.document_converter import DocumentConverter, PdfFormatOption
+
+
+def main():
+    input_doc = Path("./tests/data/2206.01062.pdf")
+
+    # Set lang=["auto"] with a tesseract OCR engine: TesseractOcrOptions, TesseractCliOcrOptions
+    # ocr_options = TesseractOcrOptions(lang=["auto"])
+    ocr_options = TesseractCliOcrOptions(lang=["auto"])
+
+    pipeline_options = PdfPipelineOptions(
+        do_ocr=True, force_full_page_ocr=True, ocr_options=ocr_options
+    )
+
+    converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+
+    doc = converter.convert(input_doc).document
+    md = doc.export_to_markdown()
+    print(md)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/translate.py b/docs/examples/translate.py
new file mode 100644
index 00000000..a36c6f71
--- /dev/null
+++ b/docs/examples/translate.py
@@ -0,0 +1,75 @@
+import logging
+import time
+from pathlib import Path
+
+from docling_core.types.doc import ImageRefMode, PictureItem, TableItem, TextItem
+
+from docling.datamodel.base_models import FigureElement, InputFormat, Table
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+
+_log = logging.getLogger(__name__)
+
+IMAGE_RESOLUTION_SCALE = 2.0
+
+
+# FIXME: put in your favorite translation code ....
+def translate(text: str, src: str = "en", dest: str = "de"):
+
+    _log.warning("!!! IMPLEMENT HERE YOUR FAVORITE TRANSLATION CODE!!!")
+    # from googletrans import Translator
+
+    # Initialize the translator
+    # translator = Translator()
+
+    # Translate text from English to German
+    # text = "Hello, how are you?"
+    # translated = translator.translate(text, src="en", dest="de")
+
+    return text
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+
+    input_doc_path = Path("./tests/data/2206.01062.pdf")
+    output_dir = Path("scratch")
+
+    # Important: For operating with page images, we must keep them, otherwise the DocumentConverter
+    # will destroy them for cleaning up memory.
+    # This is done by setting PdfPipelineOptions.images_scale, which also defines the scale of images.
+    # scale=1 correspond of a standard 72 DPI image
+    # The PdfPipelineOptions.generate_* are the selectors for the document elements which will be enriched
+    # with the image field
+    pipeline_options = PdfPipelineOptions()
+    pipeline_options.images_scale = IMAGE_RESOLUTION_SCALE
+    pipeline_options.generate_page_images = True
+    pipeline_options.generate_picture_images = True
+
+    doc_converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
+        }
+    )
+
+    start_time = time.time()
+
+    conv_res = doc_converter.convert(input_doc_path)
+    conv_doc = conv_res.document
+
+    # Save markdown with embedded pictures in original text
+    md_filename = output_dir / f"{doc_filename}-with-images-orig.md"
+    conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)
+
+    for element, _level in conv_res.document.iterate_items():
+        if isinstance(element, TextItem):
+            element.orig = element.text
+            element.text = translate(text=element.text)
+
+        elif isinstance(element, TableItem):
+            for cell in element.data.table_cells:
+                cell.text = translate(text=element.text)
+
+    # Save markdown with embedded pictures in translated text
+    md_filename = output_dir / f"{doc_filename}-with-images-translated.md"
+    conv_doc.save_as_markdown(md_filename, image_mode=ImageRefMode.EMBEDDED)
diff --git a/docs/faq.md b/docs/faq.md
index 8c8e4793..9daccae6 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -7,28 +7,7 @@ This is a collection of FAQ collected from the user questions on <https://github
 
     ### Is Python 3.13 supported?
 
-    Full support for Python 3.13 is currently waiting for [pytorch](https://github.com/pytorch/pytorch).
-
-    At the moment, no release has full support, but nightly builds are available. Docling was tested on Python 3.13 with the following steps:
-
-    ```sh
-    # Create a python 3.13 virtualenv
-    python3.13 -m venv venv
-    source ./venv/bin/activate
-
-    # Install torch nightly builds, see https://pytorch.org/
-    pip3 install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
-
-    # Install docling
-    pip3 install docling
-
-    # Run docling
-    docling --no-ocr https://arxiv.org/pdf/2408.09869
-    ```
-
-    _Note: we are disabling OCR since easyocr and the nightly torch builds have some conflicts._
-
-    Source: Issue [#136](https://github.com/DS4SD/docling/issues/136)
+    Python 3.13 is supported from Docling 2.18.0.
 
 
 ??? question "Install conflicts with numpy (python 3.13)"
@@ -123,6 +102,12 @@ This is a collection of FAQ collected from the user questions on <https://github
 
     - Update to the latest version of [certifi](https://pypi.org/project/certifi/), i.e. `pip install --upgrade certifi`
     - Use [pip-system-certs](https://pypi.org/project/pip-system-certs/) to use the latest trusted certificates on your system.
+    - Set environment variables `SSL_CERT_FILE` and `REQUESTS_CA_BUNDLE` to the value of `python -m certifi`:
+        ```
+        CERT_PATH=$(python -m certifi)
+        export SSL_CERT_FILE=${CERT_PATH}
+        export REQUESTS_CA_BUNDLE=${CERT_PATH}
+        ```
 
 
 ??? question "Which OCR languages are supported?"
@@ -145,3 +130,11 @@ This is a collection of FAQ collected from the user questions on <https://github
     pipeline_options = PdfPipelineOptions()
     pipeline_options.ocr_options.lang = ["fr", "de", "es", "en"]  # example of languages for EasyOCR
     ```
+
+
+??? Some images are missing from MS Word and Powerpoint"
+
+    ### Some images are missing from MS Word and Powerpoint
+
+    The image processing library used by Docling is able to handle embedded WMF images only on Windows platform.
+    If you are on other operaring systems, these images will be ignored.
diff --git a/docs/index.md b/docs/index.md
index c88ee7c6..18c12352 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -14,21 +14,25 @@
 [![License MIT](https://img.shields.io/github/license/DS4SD/docling)](https://opensource.org/licenses/MIT)
 [![PyPI Downloads](https://static.pepy.tech/badge/docling/month)](https://pepy.tech/projects/docling)
 
-Docling parses documents and exports them to the desired format with ease and speed.
+Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
 
 ## Features
 
-* 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
-* 📑 Advanced PDF document understanding incl. page layout, reading order & table structures
-* 🧩 Unified, expressive [DoclingDocument](./concepts/docling_document.md) representation format
-* 🤖 Plug-and-play [integrations](https://ds4sd.github.io/docling/integrations/) incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
-* 🔍 OCR support for scanned PDFs
+* 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, XLSX, HTML, images, and more
+* 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
+* 🧬 Unified, expressive [DoclingDocument][docling_document] representation format
+* ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, and lossless JSON
+* 🔒 Local execution capabilities for sensitive data and air-gapped environments
+* 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
+* 🔍 Extensive OCR support for scanned PDFs and images
 * 💻 Simple and convenient CLI
 
 ### Coming soon
 
-* ♾️ Equation & code extraction
 * 📝 Metadata extraction, including title, authors, references & language
+* 📝 Inclusion of Visual Language Models ([SmolDocling](https://huggingface.co/blog/smolervlm#smoldocling))
+* 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
+* 📝 Complex chemistry understanding (Molecular structures)
 
 ## Get started
 
@@ -42,3 +46,7 @@ Docling parses documents and exports them to the desired format with ease and sp
 ## IBM ❤️ Open Source AI
 
 Docling has been brought to you by IBM.
+
+[supported_formats]: ./supported_formats.md
+[docling_document]: ./concepts/docling_document.md
+[integrations]: ./integrations/index.md
diff --git a/docs/supported_formats.md b/docs/supported_formats.md
new file mode 100644
index 00000000..e217bb19
--- /dev/null
+++ b/docs/supported_formats.md
@@ -0,0 +1,34 @@
+Docling can parse various documents formats into a unified representation (Docling
+Document), which it can export to different formats too — check out
+[Architecture](./concepts/architecture.md) for more details.
+
+Below you can find a listing of all supported input and output formats.
+
+## Supported input formats
+
+| Format | Description |
+|--------|-------------|
+| PDF | |
+| DOCX, XLSX, PPTX | Default formats in MS Office 2007+, based on Office Open XML |
+| Markdown | |
+| AsciiDoc | |
+| HTML, XHTML | |
+| PNG, JPEG, TIFF, BMP | Image formats |
+
+Schema-specific support:
+
+| Format | Description |
+|--------|-------------|
+| USPTO XML | XML format followed by [USPTO](https://www.uspto.gov/patents) patents |
+| PMC XML | XML format followed by [PubMed Central®](https://pmc.ncbi.nlm.nih.gov/) articles |
+| Docling JSON | JSON-serialized [Docling Document](./concepts/docling_document.md) |
+
+## Supported output formats
+
+| Format | Description |
+|--------|-------------|
+| HTML | Both image embedding and referencing are supported |
+| Markdown | |
+| JSON | Lossless serialization of Docling Document |
+| Text | Plain text, i.e. without Markdown markers |
+| Doctags | |
diff --git a/docs/usage.md b/docs/usage.md
index 9a5b555a..a577a3e3 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -126,6 +126,39 @@ result = converter.convert(source)
 You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads.
 
 
+#### Use specific backend converters
+
+!!! note
+
+    This section discusses directly invoking a [backend](./concepts/architecture.md),
+    i.e. using a low-level API. This should only be done when necessary. For most cases,
+    using a `DocumentConverter` (high-level API) as discussed in the sections above
+    should suffice — and is the recommended way.
+
+By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](./supported_formats.md)).
+You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](./examples/run_with_formats.py) example.
+Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
+
+```python
+import urllib.request
+from io import BytesIO
+from docling.backend.html_backend import HTMLDocumentBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import InputDocument
+
+url = "https://en.wikipedia.org/wiki/Duck"
+text = urllib.request.urlopen(url).read()
+in_doc = InputDocument(
+    path_or_stream=BytesIO(text),
+    format=InputFormat.HTML,
+    backend=HTMLDocumentBackend,
+    filename="duck.html",
+)
+backend = HTMLDocumentBackend(in_doc=in_doc, path_or_stream=BytesIO(text))
+dl_doc = backend.convert()
+print(dl_doc.export_to_markdown())
+```
+
 ## Chunking
 
 You can chunk a Docling document using a [chunker](concepts/chunking.md), such as a
diff --git a/docs/v2.md b/docs/v2.md
index 5ccb0cdf..bf1de1c8 100644
--- a/docs/v2.md
+++ b/docs/v2.md
@@ -95,8 +95,8 @@ doc_converter = (
 
 More options are shown in the following example units:
 
-- [run_with_formats.py](../examples/run_with_formats/)
-- [custom_convert.py](../examples/custom_convert/)
+- [run_with_formats.py](examples/run_with_formats.py)
+- [custom_convert.py](examples/custom_convert.py)
 
 ### Converting documents
 
@@ -226,4 +226,4 @@ leverages the new `DoclingDocument` and provides a new, richer chunk output form
 - any applicable headings for context
 - any applicable captions for context
 
-For an example, check out [Chunking usage](../usage/#chunking).
+For an example, check out [Chunking usage](usage.md#chunking).
diff --git a/mkdocs.yml b/mkdocs.yml
index 8d9f6591..0fcc2ca4 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -56,6 +56,7 @@ nav:
     - "Docling": index.md
     - Installation: installation.md
     - Usage: usage.md
+    - Supported formats: supported_formats.md
     - FAQ: faq.md
     - Docling v2: v2.md
   - Concepts:
@@ -75,15 +76,20 @@ nav:
       - "Table export": examples/export_tables.py
       - "Multimodal export": examples/export_multimodal.py
       - "Force full page OCR": examples/full_page_ocr.py
+      - "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py
       - "Accelerator options": examples/run_with_accelerator.py
+      - "Simple translation": examples/translate.py
+      - examples/backend_xml_rag.ipynb
     - ✂️ Chunking:
-      - "Hybrid chunking": examples/hybrid_chunking.ipynb
-    - 💬 RAG / QA:
+      - examples/hybrid_chunking.ipynb
+    - 🤖 RAG with AI dev frameworks:
       - examples/rag_haystack.ipynb
-      - examples/rag_llamaindex.ipynb
       - examples/rag_langchain.ipynb
+      - examples/rag_llamaindex.ipynb
+    - 🗂️ More examples:
       - examples/rag_weaviate.ipynb
       - RAG with Granite [↗]: https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Docling_RAG.ipynb
+      - examples/rag_azuresearch.ipynb
       - examples/retrieval_qdrant.ipynb
   - Integrations:
     - Integrations: integrations/index.md
diff --git a/poetry.lock b/poetry.lock
index 06cd0bba..00901086 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -214,13 +214,13 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.3.0"
+version = "25.1.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
-    {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
+    {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
+    {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
 ]
 
 [package.extras]
@@ -231,21 +231,6 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
-[[package]]
-name = "autoflake"
-version = "2.3.1"
-description = "Removes unused imports and unused variables"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "autoflake-2.3.1-py3-none-any.whl", hash = "sha256:3ae7495db9084b7b32818b4140e6dc4fc280b712fb414f5b8fe57b0a8e85a840"},
-    {file = "autoflake-2.3.1.tar.gz", hash = "sha256:c98b75dc5b0a86459c4f01a1d32ac7eb4338ec4317a4469515ff1e687ecd909e"},
-]
-
-[package.dependencies]
-pyflakes = ">=3.0.0"
-tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
-
 [[package]]
 name = "autopep8"
 version = "2.2.0"
@@ -747,37 +732,37 @@ vision = ["Pillow (>=9.4.0)"]
 
 [[package]]
 name = "debugpy"
-version = "1.8.11"
+version = "1.8.12"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -876,94 +861,104 @@ files = [
 
 [[package]]
 name = "docling-core"
-version = "2.13.1"
+version = "2.17.0"
 description = "A python library to define and validate data types in Docling."
 optional = false
-python-versions = "<4.0,>=3.9"
-files = [
-    {file = "docling_core-2.13.1-py3-none-any.whl", hash = "sha256:5428c29efad7ae377e2d46df8fcd4185f9dbc7a6eebbea5cd0a3d6cf7162e26e"},
-    {file = "docling_core-2.13.1.tar.gz", hash = "sha256:b29e61a1a74f4df070559acce334ba9417069c15bc99cb1f6d424b1e39e063f8"},
-]
+python-versions = "^3.9"
+files = []
+develop = false
 
 [package.dependencies]
-jsonref = ">=1.1.0,<2.0.0"
-jsonschema = ">=4.16.0,<5.0.0"
-pandas = ">=2.1.4,<3.0.0"
-pillow = ">=10.3.0,<11.0.0"
-pydantic = ">=2.6.0,<2.10.0 || >2.10.0,<2.10.1 || >2.10.1,<2.10.2 || >2.10.2,<3.0.0"
+jsonref = "^1.1.0"
+jsonschema = "^4.16.0"
+latex2mathml = "^3.77.0"
+pandas = "^2.1.4"
+pillow = "^10.3.0"
+pydantic = ">=2.6.0,<3.0.0,!=2.10.0,!=2.10.1,!=2.10.2"
 pyyaml = ">=5.1,<7.0.0"
-semchunk = {version = ">=2.2.0,<3.0.0", optional = true, markers = "extra == \"chunking\""}
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-typer = ">=0.12.5,<0.13.0"
-typing-extensions = ">=4.12.2,<5.0.0"
+tabulate = "^0.9.0"
+typer = "^0.12.5"
+typing-extensions = "^4.12.2"
 
 [package.extras]
 chunking = ["semchunk (>=2.2.0,<3.0.0)", "transformers (>=4.34.0,<5.0.0)"]
 
+[package.source]
+type = "git"
+url = "ssh://git@github.com/DS4SD/docling-core.git"
+reference = "cau/add-content-layer"
+resolved_reference = "1609660791cbf83e7ffa3e5e1877f08c0ae986d7"
+
 [[package]]
 name = "docling-ibm-models"
-version = "3.1.0"
+version = "3.3.0"
 description = "This package contains the AI models used by the Docling PDF conversion package"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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 [package.dependencies]
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 jsonlines = ">=3.1.0,<4.0.0"
-numpy = ">=1.24.4,<3.0.0"
+numpy = [
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+    {version = ">=1.24.4,<2.0.0", markers = "sys_platform == \"darwin\" and platform_machine == \"x86_64\""},
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 opencv-python-headless = ">=4.6.0.66,<5.0.0.0"
 Pillow = ">=10.0.0,<11.0.0"
 safetensors = {version = ">=0.4.3,<1", extras = ["torch"]}
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 torchvision = ">=0,<1"
 tqdm = ">=4.64.0,<5.0.0"
-transformers = ">=4.42.0,<5.0.0"
+transformers = [
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+]
 
 [[package]]
 name = "docling-parse"
-version = "3.0.0"
+version = "3.1.2"
 description = "Simple package to extract text with coordinates from programmatic PDFs"
 optional = false
 python-versions = "<4.0,>=3.9"
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 ]
 
 [package.dependencies]
-autoflake = ">=2.3.1,<3.0.0"
+docling-core = ">=2.14.0,<3.0.0"
 pillow = ">=10.4.0,<11.0.0"
+pydantic = ">=2.10.5,<3.0.0"
 pywin32 = {version = ">=305", markers = "sys_platform == \"win32\""}
 tabulate = ">=0.9.0,<1.0.0"
 
@@ -1054,13 +1049,13 @@ testing = ["hatch", "pre-commit", "pytest", "tox"]
 
 [[package]]
 name = "executing"
-version = "2.1.0"
+version = "2.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1082,18 +1077,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.16.1"
+version = "3.17.0"
 description = "A platform independent file lock."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
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+docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
 typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
@@ -1142,13 +1137,13 @@ dev = ["pyTest", "pyTest-cov"]
 
 [[package]]
 name = "flatbuffers"
-version = "24.12.23"
+version = "25.1.24"
 description = "The FlatBuffers serialization format for Python"
 optional = true
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -1345,13 +1340,13 @@ test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit",
 
 [[package]]
 name = "griffe"
-version = "1.5.4"
+version = "1.5.5"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -1496,13 +1491,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.27.1"
+version = "0.28.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
-    {file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"},
+    {file = "huggingface_hub-0.28.0-py3-none-any.whl", hash = "sha256:71cff4e500efe68061d94b7f6d3114e183715088be7a90bf4dd84af83b5f5cdb"},
+    {file = "huggingface_hub-0.28.0.tar.gz", hash = "sha256:c2b18c02a47d4384763caddb4d0ab2a8fc6c16e0800d6de4d55d0a896244aba3"},
 ]
 
 [package.dependencies]
@@ -1515,13 +1510,13 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp"]
-quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
@@ -1544,13 +1539,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.6.5"
+version = "2.6.6"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "identify-2.6.5-py2.py3-none-any.whl", hash = "sha256:14181a47091eb75b337af4c23078c9d09225cd4c48929f521f3bf16b09d02566"},
-    {file = "identify-2.6.5.tar.gz", hash = "sha256:c10b33f250e5bba374fae86fb57f3adcebf1161bce7cdf92031915fd480c13bc"},
+    {file = "identify-2.6.6-py2.py3-none-any.whl", hash = "sha256:cbd1810bce79f8b671ecb20f53ee0ae8e86ae84b557de31d89709dc2a48ba881"},
+    {file = "identify-2.6.6.tar.gz", hash = "sha256:7bec12768ed44ea4761efb47806f0a41f86e7c0a5fdf5950d4648c90eca7e251"},
 ]
 
 [package.extras]
@@ -1572,13 +1567,13 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "imageio"
-version = "2.36.1"
+version = "2.37.0"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "imageio-2.36.1-py3-none-any.whl", hash = "sha256:20abd2cae58e55ca1af8a8dcf43293336a59adf0391f1917bf8518633cfc2cdf"},
-    {file = "imageio-2.36.1.tar.gz", hash = "sha256:e4e1d231f47f9a9e16100b0f7ce1a86e8856fb4d1c0fa2c4365a316f1746be62"},
+    {file = "imageio-2.37.0-py3-none-any.whl", hash = "sha256:11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed"},
+    {file = "imageio-2.37.0.tar.gz", hash = "sha256:71b57b3669666272c818497aebba2b4c5f20d5b37c81720e5e1a56d59c492996"},
 ]
 
 [package.dependencies]
@@ -1605,13 +1600,13 @@ tifffile = ["tifffile"]
 
 [[package]]
 name = "importlib-metadata"
-version = "8.5.0"
+version = "8.6.1"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
-    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
+    {file = "importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e"},
+    {file = "importlib_metadata-8.6.1.tar.gz", hash = "sha256:310b41d755445d74569f993ccfc22838295d9fe005425094fad953d7f15c8580"},
 ]
 
 [package.dependencies]
@@ -1623,7 +1618,7 @@ cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["flufl.flake8", "importlib_resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
 type = ["pytest-mypy"]
 
 [[package]]
@@ -2124,18 +2119,18 @@ transformers = ">=4.39.0"
 
 [[package]]
 name = "langchain-milvus"
-version = "0.1.7"
+version = "0.1.8"
 description = "An integration package connecting Milvus and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_milvus-0.1.7-py3-none-any.whl", hash = "sha256:8d7bef9e7524772db788f996ef9638dea431c034a36cf92a70a31d522eb8ee31"},
-    {file = "langchain_milvus-0.1.7.tar.gz", hash = "sha256:6cc4b58803c853be6766c6ed68723e244e9af101c3cd52e22ccfc05c96b2107c"},
+    {file = "langchain_milvus-0.1.8-py3-none-any.whl", hash = "sha256:adadb15ce2ceae0bc58a4444454a21fea13800c5d3750ca7e58506eecfe28ad4"},
+    {file = "langchain_milvus-0.1.8.tar.gz", hash = "sha256:cb6c2c34e1d44b84ae68c7bddbfeeba5192a3b7c046ef645aba0b011ab8e8b53"},
 ]
 
 [package.dependencies]
 langchain-core = ">=0.2.38,<0.4"
-pymilvus = ">=2.4.3,<3.0.0"
+pymilvus = ">=2.5.0,<3.0.0"
 
 [[package]]
 name = "langchain-text-splitters"
@@ -2175,6 +2170,17 @@ requests-toolbelt = ">=1.0.0,<2.0.0"
 [package.extras]
 langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
 
+[[package]]
+name = "latex2mathml"
+version = "3.77.0"
+description = "Pure Python library for LaTeX to MathML conversion"
+optional = false
+python-versions = ">=3.8.1,<4.0.0"
+files = [
+    {file = "latex2mathml-3.77.0-py3-none-any.whl", hash = "sha256:5531e18a2a9eae7c24e257118b6a444cbba253cd27ff3e81f1bd6c41e88e786e"},
+    {file = "latex2mathml-3.77.0.tar.gz", hash = "sha256:e2f501d1878f2e489c3f6f12786bef74c62f712d2770f7f3c837eb20a55d0a1e"},
+]
+
 [[package]]
 name = "lazy-loader"
 version = "0.4"
@@ -2606,13 +2612,13 @@ tqdm = "*"
 
 [[package]]
 name = "mistune"
-version = "3.1.0"
+version = "3.1.1"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mistune-3.1.0-py3-none-any.whl", hash = "sha256:b05198cf6d671b3deba6c87ec6cf0d4eb7b72c524636eddb6dbf13823b52cee1"},
-    {file = "mistune-3.1.0.tar.gz", hash = "sha256:dbcac2f78292b9dc066cd03b7a3a26b62d85f8159f2ea5fd28e55df79908d667"},
+    {file = "mistune-3.1.1-py3-none-any.whl", hash = "sha256:02106ac2aa4f66e769debbfa028509a275069dcffce0dfa578edd7b991ee700a"},
+    {file = "mistune-3.1.1.tar.gz", hash = "sha256:e0740d635f515119f7d1feb6f9b192ee60f0cc649f80a8f944f905706a21654c"},
 ]
 
 [package.dependencies]
@@ -2651,13 +2657,13 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "1.2.0"
+version = "1.3.0"
 description = "Automatically link across pages in MkDocs."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "mkdocs_autorefs-1.2.0-py3-none-any.whl", hash = "sha256:d588754ae89bd0ced0c70c06f58566a4ee43471eeeee5202427da7de9ef85a2f"},
-    {file = "mkdocs_autorefs-1.2.0.tar.gz", hash = "sha256:a86b93abff653521bda71cf3fc5596342b7a23982093915cb74273f67522190f"},
+    {file = "mkdocs_autorefs-1.3.0-py3-none-any.whl", hash = "sha256:d180f9778a04e78b7134e31418f238bba56f56d6a8af97873946ff661befffb3"},
+    {file = "mkdocs_autorefs-1.3.0.tar.gz", hash = "sha256:6867764c099ace9025d6ac24fd07b85a98335fbd30107ef01053697c8f46db61"},
 ]
 
 [package.dependencies]
@@ -2718,13 +2724,13 @@ pygments = ">2.12.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.49"
+version = "9.5.50"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.49-py3-none-any.whl", hash = "sha256:c3c2d8176b18198435d3a3e119011922f3e11424074645c24019c2dcf08a360e"},
-    {file = "mkdocs_material-9.5.49.tar.gz", hash = "sha256:3671bb282b4f53a1c72e08adbe04d2481a98f85fed392530051f80ff94a9621d"},
+    {file = "mkdocs_material-9.5.50-py3-none-any.whl", hash = "sha256:f24100f234741f4d423a9d672a909d859668a4f404796be3cf035f10d6050385"},
+    {file = "mkdocs_material-9.5.50.tar.gz", hash = "sha256:ae5fe16f3d7c9ccd05bb6916a7da7420cf99a9ce5e33debd9d40403a090d5825"},
 ]
 
 [package.dependencies]
@@ -2741,7 +2747,7 @@ regex = ">=2022.4"
 requests = ">=2.26,<3.0"
 
 [package.extras]
-git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
+git = ["mkdocs-git-committers-plugin-2 (>=1.1,<3)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
 imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
 
@@ -2803,41 +2809,15 @@ mkdocstrings = ">=0.26"
 
 [[package]]
 name = "more-itertools"
-version = "10.5.0"
+version = "10.6.0"
 description = "More routines for operating on iterables, beyond itertools"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "more-itertools-10.5.0.tar.gz", hash = "sha256:5482bfef7849c25dc3c6dd53a6173ae4795da2a41a80faea6700d9f5846c5da6"},
-    {file = "more_itertools-10.5.0-py3-none-any.whl", hash = "sha256:037b0d3203ce90cca8ab1defbbdac29d5f993fc20131f3664dc8d6acfa872aef"},
+    {file = "more-itertools-10.6.0.tar.gz", hash = "sha256:2cd7fad1009c31cc9fb6a035108509e6547547a7a738374f10bd49a09eb3ee3b"},
+    {file = "more_itertools-10.6.0-py3-none-any.whl", hash = "sha256:6eb054cb4b6db1473f6e15fcc676a08e4732548acd47c708f0e179c2c7c01e89"},
 ]
 
-[[package]]
-name = "mpire"
-version = "2.10.2"
-description = "A Python package for easy multiprocessing, but faster than multiprocessing"
-optional = false
-python-versions = "*"
-files = [
-    {file = "mpire-2.10.2-py3-none-any.whl", hash = "sha256:d627707f7a8d02aa4c7f7d59de399dec5290945ddf7fbd36cbb1d6ebb37a51fb"},
-    {file = "mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97"},
-]
-
-[package.dependencies]
-multiprocess = [
-    {version = "*", optional = true, markers = "python_version < \"3.11\" and extra == \"dill\""},
-    {version = ">=0.70.15", optional = true, markers = "python_version >= \"3.11\" and extra == \"dill\""},
-]
-pygments = ">=2.0"
-pywin32 = {version = ">=301", markers = "platform_system == \"Windows\""}
-tqdm = ">=4.27"
-
-[package.extras]
-dashboard = ["flask"]
-dill = ["multiprocess", "multiprocess (>=0.70.15)"]
-docs = ["docutils (==0.17.1)", "sphinx (==3.2.1)", "sphinx-autodoc-typehints (==1.11.0)", "sphinx-rtd-theme (==0.5.0)", "sphinx-versions (==1.0.1)", "sphinxcontrib-images (==0.9.2)"]
-testing = ["ipywidgets", "multiprocess", "multiprocess (>=0.70.15)", "numpy", "pywin32 (>=301)", "rich"]
-
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -3077,13 +3057,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.5"
+version = "7.16.6"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.16.5-py3-none-any.whl", hash = "sha256:e12eac052d6fd03040af4166c563d76e7aeead2e9aadf5356db552a1784bd547"},
-    {file = "nbconvert-7.16.5.tar.gz", hash = "sha256:c83467bb5777fdfaac5ebbb8e864f300b277f68692ecc04d6dab72f2d8442344"},
+    {file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"},
+    {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
 ]
 
 [package.dependencies]
@@ -3255,6 +3235,51 @@ files = [
     {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
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 [[package]]
@@ -3603,6 +3628,18 @@ files = [
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.6.2"
+description = "NVIDIA cuSPARSELt"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "nvidia-nccl-cu12"
 version = "2.19.3"
@@ -3637,14 +3674,14 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.85"
+version = "12.8.61"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [[package]]
@@ -3768,54 +3805,54 @@ sympy = "*"
 
 [[package]]
 name = "opencv-python"
-version = "4.10.0.84"
+version = "4.11.0.86"
 description = "Wrapper package for OpenCV python bindings."
 optional = true
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
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 ]
 
 [[package]]
 name = "opencv-python-headless"
-version = "4.10.0.84"
+version = "4.11.0.86"
 description = "Wrapper package for OpenCV python bindings."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
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 ]
 
 [package.extras]
@@ -4571,13 +4612,13 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.10.4"
+version = "2.10.6"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4777,13 +4818,13 @@ testutils = ["gitpython (>3)"]
 
 [[package]]
 name = "pymdown-extensions"
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+version = "10.14.2"
 description = "Extension pack for Python Markdown."
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 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4795,13 +4836,13 @@ extra = ["pygments (>=2.19.1)"]
 
 [[package]]
 name = "pymilvus"
-version = "2.5.3"
+version = "2.5.4"
 description = "Python Sdk for Milvus"
 optional = false
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 [[package]]
 name = "pypdfium2"
@@ -5382,120 +5419,120 @@ pyyaml = "*"
 
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+
 [[package]]
 name = "secretstorage"
 version = "3.3.3"
@@ -6058,21 +6153,6 @@ files = [
 cryptography = ">=2.0"
 jeepney = ">=0.6"
 
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 [[package]]
 name = "semver"
 version = "2.13.0"
@@ -6086,13 +6166,13 @@ files = [
 
 [[package]]
 name = "sentence-transformers"
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 description = "State-of-the-Art Text Embeddings"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -6113,13 +6193,13 @@ train = ["accelerate (>=0.20.3)", "datasets"]
 
 [[package]]
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+version = "75.8.0"
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+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
+
 [[package]]
 name = "tokenizers"
 version = "0.21.0"
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 [package.dependencies]
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 [package.extras]
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-optree = ["optree (>=0.12.0)"]
+optree = ["optree (>=0.13.0)"]
 
 [[package]]
 name = "torchvision"
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 [[package]]
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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 python-versions = ">=3.9.0"
 files = [
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 [package.dependencies]
@@ -6774,16 +7047,16 @@ tqdm = ">=4.27"
 
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 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -6804,17 +7077,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 timm = ["timm (<=1.0.11)"]
 tokenizers = ["tokenizers (>=0.21,<0.22)"]
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 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -6843,21 +7116,18 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
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 tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
@@ -6952,13 +7222,13 @@ files = [
 
 [[package]]
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-version = "2024.2"
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 description = "Provider of IANA time zone data"
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 python-versions = ">=2"
 files = [
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 [[package]]
@@ -7067,13 +7337,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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 description = "Virtual Python Environment builder"
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "xlsxwriter"
-version = "3.2.0"
+version = "3.2.2"
 description = "A Python module for creating Excel XLSX files."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "XlsxWriter-3.2.0-py3-none-any.whl", hash = "sha256:ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e"},
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+    {file = "XlsxWriter-3.2.2-py3-none-any.whl", hash = "sha256:272ce861e7fa5e82a4a6ebc24511f2cb952fde3461f6c6e1a1e81d3272db1471"},
+    {file = "xlsxwriter-3.2.2.tar.gz", hash = "sha256:befc7f92578a85fed261639fb6cde1fd51b79c5e854040847dde59d4317077dc"},
 ]
 
 [[package]]
@@ -7514,4 +7798,4 @@ tesserocr = ["tesserocr"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "33da0f74647611b3d6160376dd172a22b7a9ca9f384c5c36c5dd863444f0dede"
+content-hash = "36a0b681728cfdaa0aea8bdd5a55e751d535b0feed3c08f4242e82da1526772d"
diff --git a/pyproject.toml b/pyproject.toml
index e84fa072..4310ad88 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "docling"
-version = "2.15.1"  # DO NOT EDIT, updated automatically
+version = "2.17.0"  # DO NOT EDIT, updated automatically
 description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications."
 authors = ["Christoph Auer <cau@zurich.ibm.com>", "Michele Dolfi <dol@zurich.ibm.com>", "Maxim Lysak <mly@zurich.ibm.com>", "Nikos Livathinos <nli@zurich.ibm.com>", "Ahmed Nassar <ahn@zurich.ibm.com>", "Panos Vagenas <pva@zurich.ibm.com>", "Peter Staar <taa@zurich.ibm.com>"]
 license = "MIT"
@@ -25,11 +25,11 @@ packages = [{include = "docling"}]
 # actual dependencies:
 ######################
 python = "^3.9"
-docling-core = { version = "^2.13.1", extras = ["chunking"] }
 pydantic = "^2.0.0"
-docling-ibm-models = "^3.1.0"
+docling-core = {git = "ssh://git@github.com/DS4SD/docling-core.git", rev = "cau/add-content-layer"}
+docling-ibm-models = "^3.3.0"
 deepsearch-glm = "^1.0.0"
-docling-parse = "^3.0.0"
+docling-parse = "^3.1.0"
 filetype = "^1.2.0"
 pypdfium2 = "^4.30.0"
 pydantic-settings = "^2.3.0"
@@ -39,7 +39,10 @@ easyocr = "^1.7"
 tesserocr = { version = "^2.7.1", optional = true }
 certifi = ">=2024.7.4"
 rtree = "^1.3.0"
-scipy = "^1.6.0"
+scipy = [
+  { version = "^1.6.0", markers = "python_version >= '3.10'" },
+  { version = ">=1.6.0,<1.14.0", markers = "python_version < '3.10'" }
+]
 typer = "^0.12.5"
 python-docx = "^1.1.2"
 python-pptx = "^1.0.2"
@@ -56,6 +59,7 @@ onnxruntime = [
   { version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" },
   { version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" }
 ]
+pillow = "^10.0.0"
 
 [tool.poetry.group.dev.dependencies]
 black = {extras = ["jupyter"], version = "^24.4.2"}
diff --git a/tests/data/amt_handbook_sample.pdf b/tests/data/amt_handbook_sample.pdf
new file mode 100644
index 00000000..058513a3
Binary files /dev/null and b/tests/data/amt_handbook_sample.pdf differ
diff --git a/tests/data/code_and_formula.pdf b/tests/data/code_and_formula.pdf
new file mode 100644
index 00000000..82cd8343
Binary files /dev/null and b/tests/data/code_and_formula.pdf differ
diff --git a/tests/data/docx/unit_test_headers_numbered.docx b/tests/data/docx/unit_test_headers_numbered.docx
new file mode 100644
index 00000000..259125c3
Binary files /dev/null and b/tests/data/docx/unit_test_headers_numbered.docx differ
diff --git a/tests/data/docx/word_tables.docx b/tests/data/docx/word_tables.docx
new file mode 100644
index 00000000..1513796e
Binary files /dev/null and b/tests/data/docx/word_tables.docx differ
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.json b/tests/data/groundtruth/docling_v1/2203.01017v2.json
index ca13c60c..6a30a45d 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.json
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.json
@@ -1 +1 @@
-{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "2203.01017v2.pdf", "filename-prov": null, "document-hash": "00be757f6bc94e7f75134e02e196d1c73ff960d52abb081a93104bba5a6470ff", "#-pages": 16, "collection-name": null, "description": null, "page-hashes": [{"hash": "56046b8a75e14ce81bf22f568f726b2bd7b8f82a885dbff3260d8945699796e4", "model": "default", "page": 1}, {"hash": "80f9116b4f8c9b08dc9763877dabaf65affef9c4a9a0f91a3673e8360aa95713", "model": "default", "page": 2}, {"hash": "8b422eca6675fcaa4919295eb9af624e581041dad8a5903bec7cb26254f2b59b", "model": "default", "page": 3}, {"hash": "a7f7a5799d183e4f3d0d8e917c3df1acd344e0abfaedebb9ed04f2f67844ad95", "model": "default", "page": 4}, {"hash": "14036095ac3b7fb802ff5c061cf5584f482de800aff7ed4eccfb67e5a89e1ba2", "model": "default", "page": 5}, {"hash": "633c440068e406a17f913cac0c2e3f0606f66111994bd2940726a56ea37274a0", "model": "default", "page": 6}, {"hash": "fb67646dad9c7255e55f305b59fb6f697fcf26875e085b63ac0729916ce60b6c", "model": "default", "page": 7}, {"hash": "acc79b977714a917605f6530c1df05f73f4cc0aa5d73b506fffe6287ae19a807", "model": "default", "page": 8}, {"hash": "bdb376928a3150909023df34ff94cd1eb12e1f90ae03a72834b74433ef498205", "model": "default", "page": 9}, {"hash": "df60cff4949de8851338c4fd85ad43f534cee1da4772f36b74b2341cad6ec5c9", "model": "default", "page": 10}, {"hash": "3df470edc2c1a275cfa920f7487a89fcb1825a9b009e85386b199cbfe80aff73", "model": "default", "page": 11}, {"hash": "f986169f2c0ff7997ccec2d71833cea7c5df4a641b92996c3f8fba9563441ad1", "model": "default", "page": 12}, {"hash": "ba03af2311ea8d68bf53cccda386ebf4ed68f14943fc421417799401a2afe95a", "model": "default", "page": 13}, {"hash": "1d63990cced2905e29696a09ad1bca47fcd15d584be32b4c7ab76c2f47f75d92", "model": "default", "page": 14}, {"hash": "fc40b9fb3698f24af7beda03b7afac10c3fcc6c73e83b4c6159785ea2991e2c4", "model": "default", "page": 15}, {"hash": "96080fce6eb8572fe319782f353a67661947f48e67607b1ffd8c01d617d075a7", "model": "default", "page": 16}]}, "main-text": [{"prov": [{"bbox": [18.340221405029297, 231.99996948242188, 36.339778900146484, 584.1799926757812], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [96.3010025024414, 672.0686645507812, 498.9270935058594, 684.9658813476562], "page": 1, "span": [0, 61], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [142.4770050048828, 620.6796264648438, 452.7502746582031, 645.3146362304688], "page": 1, "span": [0, 73], "__ref_s3_data": null}], "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [208.123, 607.57446, 378.73257, 616.03876], "page": 1, "span": [0, 35], "__ref_s3_data": null}], "text": "{ ahn,nli,mly,taa } @zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.99497985839844, 565.769287109375, 190.48028564453125, 576.5170288085938], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "Abstract", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [315.5670166015625, 565.2451782226562, 408.4407043457031, 573.9931640625], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "a. Picture of a table:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 241.30950927734375, 126.94803619384766, 252.05723571777344], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "1. Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 78.84822082519531, 286.3650817871094, 231.216796875], "page": 1, "span": [0, 712], "__ref_s3_data": null}], "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [50.111976623535156, 279.00335693359375, 286.3651123046875, 550.6049194335938], "page": 1, "span": [0, 1320], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [315.5670166015625, 458.7572021484375, 486.4019470214844, 478.3052062988281], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [315.5670166015625, 363.0691833496094, 491.1912536621094, 371.81719970703125], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "- c. Structure predicted by TableFormer:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [308.86199951171875, 232.7270965576172, 545.1151733398438, 277.4996337890625], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [308.86199951171875, 126.95307159423828, 545.1151733398438, 207.59063720703125], "page": 1, "span": [0, 363], "__ref_s3_data": null}], "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84806823730469, 545.1151123046875, 123.61963653564453], "page": 1, "span": [0, 229], "__ref_s3_data": null}], "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.9600715637207, 300.102294921875, 57.866634368896484], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.36505126953125, 716.7916259765625], "page": 2, "span": [0, 75], "__ref_s3_data": null}], "text": "considered as a solved problem, given enough ground-truth data to train on.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 563.9699096679688, 286.3651428222656, 692.4285888671875], "page": 2, "span": [0, 626], "__ref_s3_data": null}], "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 420.054931640625, 286.3651123046875, 560.4684448242188], "page": 2, "span": [0, 643], "__ref_s3_data": null}], "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 359.8269958496094, 286.3665771484375, 416.5534973144531], "page": 2, "span": [0, 242], "__ref_s3_data": null}], "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [61.56901550292969, 302.6770324707031, 286.3648986816406, 347.568115234375], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "- \u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.56901550292969, 245.0740509033203, 286.3648986816406, 289.9661560058594], "page": 2, "span": [0, 181], "__ref_s3_data": null}], "text": "- \u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 199.4270477294922, 286.36492919921875, 232.3631591796875], "page": 2, "span": [0, 106], "__ref_s3_data": null}], "text": "- \u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569007873535156, 153.779052734375, 286.3650817871094, 186.5966033935547], "page": 2, "span": [0, 131], "__ref_s3_data": null}], "text": "- \u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 96.63004302978516, 286.3651123046875, 141.401611328125], "page": 2, "span": [0, 231], "__ref_s3_data": null}], "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [60.97100067138672, 79.27845764160156, 183.7305450439453, 86.40372467041016], "page": 2, "span": [0, 40], "__ref_s3_data": null}], "text": "$^{1}$https://github.com/IBM/SynthTabNet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96015548706055, 300.102294921875, 57.86671829223633], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 683.9750366210938, 545.1151123046875, 716.7916259765625], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 659.5203247070312, 498.28021240234375, 670.26806640625], "page": 2, "span": [0, 37], "__ref_s3_data": null}], "text": "2. Previous work and State of the Art", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 461.54498291015625, 545.1151733398438, 649.7786254882812], "page": 2, "span": [0, 901], "__ref_s3_data": null}], "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 341.9270935058594, 545.115234375, 458.4305419921875], "page": 2, "span": [0, 552], "__ref_s3_data": null}], "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84815216064453, 545.1168823242188, 338.9322204589844], "page": 2, "span": [0, 1262], "__ref_s3_data": null}], "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 250.15101623535156, 716.7916259765625], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "tag-decoder which is constrained to the table-tags.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 516.5458984375, 286.3651428222656, 704.7806396484375], "page": 3, "span": [0, 864], "__ref_s3_data": null}], "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 301.297119140625, 286.3651123046875, 513.56103515625], "page": 3, "span": [0, 1007], "__ref_s3_data": null}], "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 169.733154296875, 286.36627197265625, 298.3112487792969], "page": 3, "span": [0, 619], "__ref_s3_data": null}], "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 145.30743408203125, 105.22545623779297, 156.05516052246094], "page": 3, "span": [0, 11], "__ref_s3_data": null}], "text": "3. Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 78.84813690185547, 286.3650817871094, 135.57470703125], "page": 3, "span": [0, 281], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96023941040039, 300.102294921875, 57.86680221557617], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 503.3020935058594, 545.1151123046875, 524.1636352539062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.86199951171875, 465.6200866699219, 437.27001953125, 474.5266418457031], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 164.6382598876953, 545.1151733398438, 460.4686279296875], "page": 3, "span": [0, 1400], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84823608398438, 545.1151123046875, 159.48580932617188], "page": 3, "span": [0, 406], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.3651123046875, 716.7916259765625], "page": 4, "span": [0, 93], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 478.8949279785156, 286.3651428222656, 691.0396118164062], "page": 4, "span": [0, 983], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 357.50103759765625, 286.3651123046875, 474.0044860839844], "page": 4, "span": [0, 571], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 164.37611389160156, 286.3665466308594, 352.610595703125], "page": 4, "span": [0, 941], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84810638427734, 286.3651123046875, 159.4856719970703], "page": 4, "span": [0, 405], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209716796875, 48.96018600463867, 300.1022644042969, 57.86674880981445], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 567.6110229492188, 545.1150512695312, 624.338623046875], "page": 4, "span": [0, 267], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.86199951171875, 497.6080322265625, 545.1151733398438, 542.3795776367188], "page": 4, "span": [0, 210], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.8169860839844, 485.321044921875, 542.7439575195312, 494.22760009765625], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 460.0683288574219, 444.9360656738281, 470.8160400390625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 345.5131530761719, 545.115234375, 450.06060791015625], "page": 4, "span": [0, 504], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 324.45367431640625, 420.16058349609375, 334.30572509765625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8619689941406, 127.00019073486328, 545.11572265625, 315.2347106933594], "page": 4, "span": [0, 907], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84818267822266, 545.1151123046875, 123.73930358886719], "page": 4, "span": [0, 223], "__ref_s3_data": null}], "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 567.0330810546875, 545.1084594726562, 588.0142211914062], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [50.11199951171875, 111.72905731201172, 286.365966796875, 264.2171936035156], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.86199951171875, 497.69305419921875, 545.1150512695312, 542.465576171875], "page": 5, "span": [0, 227], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 378.0381774902344, 545.1151123046875, 494.6601867675781], "page": 5, "span": [0, 563], "__ref_s3_data": null}], "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 246.4272918701172, 545.1151123046875, 374.8857421875], "page": 5, "span": [0, 592], "__ref_s3_data": null}], "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 138.727294921875, 545.1151123046875, 243.39540100097656], "page": 5, "span": [0, 483], "__ref_s3_data": null}], "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 78.84827423095703, 545.1150512695312, 135.57484436035156], "page": 5, "span": [0, 286], "__ref_s3_data": null}], "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209411621094, 48.96027755737305, 300.10223388671875, 57.86684036254883], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 636.1539916992188, 286.3651428222656, 716.7916259765625], "page": 6, "span": [0, 380], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 551.7369384765625, 286.3651123046875, 632.3755493164062], "page": 6, "span": [0, 371], "__ref_s3_data": null}], "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 347.76910400390625, 286.36572265625, 548.0780639648438], "page": 6, "span": [0, 985], "__ref_s3_data": null}], "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112022399902344, 323.12811279296875, 286.364990234375, 343.9896545410156], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as following:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [124.33001708984375, 274.92828369140625, 286.3624267578125, 298.71905517578125], "page": 6, "span": [0, 84], "__ref_s3_data": null}], "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112030029296875, 251.78411865234375, 281.596923828125, 261.4079895019531], "page": 6, "span": [0, 76], "__ref_s3_data": null}], "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11204528808594, 225.33538818359375, 171.9833526611328, 236.08311462402344], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. Experimental Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 205.8836212158203, 179.17501831054688, 215.7356719970703], "page": 6, "span": [0, 27], "__ref_s3_data": null}], "text": "5.1. Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 151.4931182861328, 286.36517333984375, 196.2656707763672], "page": 6, "span": [0, 207], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.66104888916016, 113.60411834716797, 286.3624572753906, 138.1719970703125], "page": 6, "span": [0, 77], "__ref_s3_data": null}], "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112060546875, 78.8481216430664, 286.3651428222656, 99.70968627929688], "page": 6, "span": [0, 117], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.12103271484375, 48.96010971069336, 300.1023254394531, 57.86667251586914], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.862060546875, 683.97509765625, 545.115234375, 716.7916870117188], "page": 6, "span": [0, 156], "__ref_s3_data": null}], "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 463.6259460449219, 545.1152954101562, 675.7706298828125], "page": 6, "span": [0, 1024], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 362.83001708984375, 545.1151733398438, 455.4224853515625], "page": 6, "span": [0, 419], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 238.12310791015625, 545.115234375, 354.6255798339844], "page": 6, "span": [0, 528], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 202.5936279296875, 397.44281005859375, 212.4456787109375], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 119.86811065673828, 545.1151733398438, 188.55067443847656], "page": 6, "span": [0, 299], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 78.84710693359375, 545.115234375, 111.6646728515625], "page": 6, "span": [0, 155], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.74658203125, 167.89825439453125, 717.5986328125], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 653.8770141601562, 286.3651123046875, 698.6495971679688], "page": 7, "span": [0, 192], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.218994140625, 619.26123046875, 286.3623962402344, 641.6820068359375], "page": 7, "span": [0, 99], "__ref_s3_data": null}], "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11198425292969, 578.02099609375, 286.36285400390625, 610.9970092773438], "page": 7, "span": [0, 162], "__ref_s3_data": null}], "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 557.3284912109375, 170.45169067382812, 567.1805419921875], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. Quantitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 395.862060546875, 286.3651428222656, 548.35009765625], "page": 7, "span": [0, 723], "__ref_s3_data": null}], "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 178.705078125, 286.3651123046875, 199.56663513183594], "page": 7, "span": [0, 101], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [50.11199951171875, 166.7500762939453, 261.7873229980469, 175.65663146972656], "page": 7, "span": [0, 50], "__ref_s3_data": null}], "text": "FT: Model was trained on PubTabNet then finetuned.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84806823730469, 286.3659973144531, 147.6501922607422], "page": 7, "span": [0, 346], "__ref_s3_data": null}], "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.960079193115234, 300.102294921875, 57.866641998291016], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 564.4229125976562, 545.1151733398438, 716.7916259765625], "page": 7, "span": [0, 737], "__ref_s3_data": null}], "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 454.68914794921875, 545.1151733398438, 475.5506896972656], "page": 7, "span": [0, 94], "__ref_s3_data": null}], "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [308.8619689941406, 271.8323059082031, 545.1156616210938, 424.3202819824219], "page": 7, "span": [0, 715], "__ref_s3_data": null}], "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 102.32206726074219, 545.1151733398438, 135.13864135742188], "page": 7, "span": [0, 148], "__ref_s3_data": null}], "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/5"}, {"prov": [{"bbox": [53.28603744506836, 705.4392700195312, 61.550289154052734, 713.3124389648438], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "- a.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [65.68241882324219, 705.4392700195312, 499.5556335449219, 713.3124389648438], "page": 8, "span": [0, 105], "__ref_s3_data": null}], "text": "- Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.81178283691406, 689.845703125, 284.3459167480469, 697.7188720703125], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.830810546875, 689.845703125, 431.0911865234375, 697.7188720703125], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [53.81178283691406, 575.8935546875, 385.93450927734375, 583.7667236328125], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"prov": [{"bbox": [380.42730712890625, 493.39715576171875, 549.4217529296875, 499.69573974609375], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"prov": [{"bbox": [50.11199951171875, 426.3501281738281, 545.11376953125, 471.1226501464844], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [62.595001220703125, 324.3650817871094, 532.6304931640625, 333.2716369628906], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [50.11199951171875, 290.7525939941406, 163.75579833984375, 300.6046447753906], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. Qualitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84805297851562, 286.3651123046875, 255.1266326904297], "page": 8, "span": [0, 866], "__ref_s3_data": null}], "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 290.5433654785156, 460.8484802246094, 301.29107666015625], "page": 8, "span": [0, 27], "__ref_s3_data": null}], "text": "6. Future Work & Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 138.69407653808594, 545.1151733398438, 279.10662841796875], "page": 8, "span": [0, 640], "__ref_s3_data": null}], "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 109.15335845947266, 364.4058532714844, 119.90107727050781], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [313.3450012207031, 79.06324768066406, 545.1134033203125, 98.0382080078125], "page": 8, "span": [0, 121], "__ref_s3_data": null}], "text": "- [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD , KDD '18, pages 774-782, New York, NY, USA, 2018. ACM. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 333.3085632324219, 286.3638916015625, 407.0774841308594], "page": 10, "span": [0, 366], "__ref_s3_data": null}], "text": "- [31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30 , pages 5998-6008. Curran Associates, Inc., 2017. 5", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 289.1075744628906, 286.36334228515625, 330.0005187988281], "page": 10, "span": [0, 221], "__ref_s3_data": null}], "text": "- [32] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2015. 2", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11201477050781, 244.90756225585938, 286.3633728027344, 285.7995300292969], "page": 10, "span": [0, 217], "__ref_s3_data": null}], "text": "- [33] Wenyuan Xue, Qingyong Li, and Dacheng Tao. Res2tim: reconstruct syntactic structures from table images. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 749-755. IEEE, 2019. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112022399902344, 200.70655822753906, 286.3633728027344, 241.59951782226562], "page": 10, "span": [0, 190], "__ref_s3_data": null}], "text": "- [34] Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. Tgrnet: A table graph reconstruction network for table structure recognition. arXiv preprint arXiv:2106.10598 , 2021. 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112030029296875, 156.50555419921875, 286.3634033203125, 197.3985137939453], "page": 10, "span": [0, 220], "__ref_s3_data": null}], "text": "- [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4651-4659, 2016. 4", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112022399902344, 101.34652709960938, 286.3633728027344, 153.197509765625], "page": 10, "span": [0, 280], "__ref_s3_data": null}], "text": "- [36] Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV) , 2021. 2, 3", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11201477050781, 79.06353759765625, 286.36334228515625, 98.03849792480469], "page": 10, "span": [0, 106], "__ref_s3_data": null}], "text": "- [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model,", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6300048828125, 48.960445404052734, 302.59259033203125, 57.867008209228516], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [328.781005859375, 675.2245483398438, 545.1145629882812, 716.1165161132812], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 630.392578125, 545.1133422851562, 671.2855224609375], "page": 10, "span": [0, 221], "__ref_s3_data": null}], "text": "- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [132.8419952392578, 656.4699096679688, 465.37591552734375, 681.4251098632812], "page": 11, "span": [0, 83], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 620.0913696289062, 175.96437072753906, 630.839111328125], "page": 11, "span": [0, 26], "__ref_s3_data": null}], "text": "1. Details on the datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 601.1686401367188, 150.364013671875, 611.0206909179688], "page": 11, "span": [0, 21], "__ref_s3_data": null}], "text": "1.1. Data preparation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 403.8451843261719, 286.3651428222656, 592.0797119140625], "page": 11, "span": [0, 931], "__ref_s3_data": null}], "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 164.54029846191406, 286.3651123046875, 400.5947265625], "page": 11, "span": [0, 1149], "__ref_s3_data": null}], "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 140.42730712890625, 286.3649597167969, 161.28985595703125], "page": 11, "span": [0, 92], "__ref_s3_data": null}], "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 119.7578125, 153.60784912109375, 129.60986328125], "page": 11, "span": [0, 23], "__ref_s3_data": null}], "text": "1.2. Synthetic datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 77.852294921875, 286.36505126953125, 110.66886901855469], "page": 11, "span": [0, 167], "__ref_s3_data": null}], "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 584.572265625, 545.1151123046875, 629.3448486328125], "page": 11, "span": [0, 221], "__ref_s3_data": null}], "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 559.9032592773438, 545.1150512695312, 580.7648315429688], "page": 11, "span": [0, 89], "__ref_s3_data": null}], "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 475.45721435546875, 545.1151123046875, 556.0947875976562], "page": 11, "span": [0, 373], "__ref_s3_data": null}], "text": "- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 343.19134521484375, 545.1151733398438, 471.6497802734375], "page": 11, "span": [0, 573], "__ref_s3_data": null}], "text": "- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 294.61138916015625, 545.1151733398438, 339.3839111328125], "page": 11, "span": [0, 195], "__ref_s3_data": null}], "text": "- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 246.0314178466797, 545.1152954101562, 290.803955078125], "page": 11, "span": [0, 218], "__ref_s3_data": null}], "text": "- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 185.4964141845703, 545.1151733398438, 242.22396850585938], "page": 11, "span": [0, 238], "__ref_s3_data": null}], "text": "- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 145.01368713378906, 545.1087646484375, 169.70941162109375], "page": 11, "span": [0, 47], "__ref_s3_data": null}], "text": "2. Prediction post-processing for PDF documents", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 77.85139465332031, 545.1151733398438, 134.57896423339844], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [292.63104248046875, 48.96039962768555, 302.5936279296875, 57.86696243286133], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 605.6360473632812, 545.1137084960938, 626.4976196289062], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [61.569000244140625, 560.20703125, 286.3651123046875, 581.068603515625], "page": 12, "span": [0, 61], "__ref_s3_data": null}], "text": "- \u00b7 TableFormer output does not include the table cell content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 527.0670166015625, 286.3651428222656, 547.9285888671875], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "- \u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 396.2931213378906, 286.3651123046875, 512.7965698242188], "page": 12, "span": [0, 545], "__ref_s3_data": null}], "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 372.068115234375, 286.3649597167969, 392.9306640625], "page": 12, "span": [0, 68], "__ref_s3_data": null}], "text": "Here is a step-by-step description of the prediction postprocessing:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 335.8881530761719, 286.3650817871094, 368.7046813964844], "page": 12, "span": [0, 173], "__ref_s3_data": null}], "text": "- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 287.7532043457031, 286.36505126953125, 332.52471923828125], "page": 12, "span": [0, 187], "__ref_s3_data": null}], "text": "- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 263.5272216796875, 286.36492919921875, 284.3897705078125], "page": 12, "span": [0, 97], "__ref_s3_data": null}], "text": "- 3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 227.34722900390625, 286.3651123046875, 260.164794921875], "page": 12, "span": [0, 131], "__ref_s3_data": null}], "text": "- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 191.16722106933594, 286.3650817871094, 223.98377990722656], "page": 12, "span": [0, 169], "__ref_s3_data": null}], "text": "- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [110.70498657226562, 137.89439392089844, 286.3623962402344, 168.5640869140625], "page": 12, "span": [0, 81], "__ref_s3_data": null}], "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11199951171875, 103.07321166992188, 286.36199951171875, 124.6520767211914], "page": 12, "span": [0, 103], "__ref_s3_data": null}], "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84821319580078, 286.3649597167969, 99.70977783203125], "page": 12, "span": [0, 110], "__ref_s3_data": null}], "text": "- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 536.2962036132812, 545.1151733398438, 581.0687866210938], "page": 12, "span": [0, 183], "__ref_s3_data": null}], "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 512.0361938476562, 545.114990234375, 532.8977661132812], "page": 12, "span": [0, 91], "__ref_s3_data": null}], "text": "- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 404.08929443359375, 545.1151123046875, 508.6367492675781], "page": 12, "span": [0, 471], "__ref_s3_data": null}], "text": "- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 332.00836181640625, 545.1151733398438, 400.6898498535156], "page": 12, "span": [0, 311], "__ref_s3_data": null}], "text": "- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 224.06141662597656, 545.1151733398438, 328.6089172363281], "page": 12, "span": [0, 503], "__ref_s3_data": null}], "text": "- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 187.8454132080078, 545.1168823242188, 220.66197204589844], "page": 12, "span": [0, 113], "__ref_s3_data": null}], "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 163.58441162109375, 545.1150512695312, 184.44696044921875], "page": 12, "span": [0, 101], "__ref_s3_data": null}], "text": "- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 127.3694076538086, 545.1150512695312, 160.18597412109375], "page": 12, "span": [0, 117], "__ref_s3_data": null}], "text": "- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 103.10841369628906, 545.114990234375, 123.969970703125], "page": 12, "span": [0, 107], "__ref_s3_data": null}], "text": "- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 78.84840393066406, 545.1151733398438, 99.70997619628906], "page": 12, "span": [0, 118], "__ref_s3_data": null}], "text": "- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6310729980469, 48.96040725708008, 302.5936584472656, 57.86697006225586], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 88.84658813476562, 716.7916259765625], "page": 13, "span": [0, 10], "__ref_s3_data": null}], "text": "phan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 683.9750366210938, 286.3649597167969, 704.8366088867188], "page": 13, "span": [0, 76], "__ref_s3_data": null}], "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 660.2941284179688, 286.364990234375, 680.8369140625], "page": 13, "span": [0, 97], "__ref_s3_data": null}], "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/8"}, {"name": "Table", "type": "table", "$ref": "#/tables/9"}, {"name": "Table", "type": "table", "$ref": "#/tables/10"}, {"prov": [{"bbox": [63.340999603271484, 281.0370788574219, 273.1334228515625, 289.9436340332031], "page": 13, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/11"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/12"}, {"name": "Table", "type": "table", "$ref": "#/tables/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/14"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [308.86199951171875, 464.54010009765625, 545.1151123046875, 485.4016418457031], "page": 13, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/15"}, {"name": "Table", "type": "table", "$ref": "#/tables/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/18"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [312.3429870605469, 102.60006713867188, 541.63232421875, 111.50663757324219], "page": 13, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/19"}, {"name": "Table", "type": "table", "$ref": "#/tables/20"}, {"prov": [{"bbox": [50.11199951171875, 414.36810302734375, 286.3650817871094, 435.2296447753906], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"name": "Table", "type": "table", "$ref": "#/tables/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/23"}, {"prov": [{"bbox": [54.61899948120117, 111.27507781982422, 281.85589599609375, 120.181640625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"prov": [{"bbox": [292.6309814453125, 48.96007537841797, 302.59356689453125, 57.86663818359375], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/24"}, {"name": "Table", "type": "table", "$ref": "#/tables/25"}, {"name": "Table", "type": "table", "$ref": "#/tables/26"}, {"prov": [{"bbox": [315.7900085449219, 411.4090881347656, 538.1852416992188, 420.3156433105469], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/27"}, {"name": "Table", "type": "table", "$ref": "#/tables/28"}, {"name": "Table", "type": "table", "$ref": "#/tables/29"}, {"prov": [{"bbox": [344.9849853515625, 99.54707336425781, 508.9893493652344, 108.45364379882812], "page": 14, "span": [0, 40], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/30"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/31"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/18"}, {"name": "Table", "type": "table", "$ref": "#/tables/32"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/19"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/20"}, {"prov": [{"bbox": [84.23300170898438, 138.7420654296875, 252.24224853515625, 147.64862060546875], "page": 15, "span": [0, 41], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/33"}, {"prov": [{"bbox": [292.6309814453125, 48.9600944519043, 302.59356689453125, 57.86665725708008], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/34"}, {"name": "Table", "type": "table", "$ref": "#/tables/35"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/36"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/22"}, {"prov": [{"bbox": [308.8619689941406, 118.20308685302734, 545.1151123046875, 139.0646514892578], "page": 15, "span": [0, 106], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/37"}, {"prov": [{"bbox": [50.11199951171875, 262.80108642578125, 545.1138305664062, 283.6626281738281], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/23"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 16, "span": [0, 2], "__ref_s3_data": null}], "text": "16", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [315.6535949707031, 489.1985168457031, 537.1474609375, 563.2764892578125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [314.78167724609375, 381.9505615234375, 539.1802978515625, 453.9347229003906], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [312.1036071777344, 541.39013671875, 550.3890380859375, 713.55908203125], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [74.30531311035156, 608.298828125, 519.980224609375, 714.0887451171875], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.03328323364258, 284.3311767578125, 285.3731994628906, 534.3345947265625], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [49.97503662109375, 604.4212646484375, 301.6350402832031, 688.2876586914062], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [305.5844421386719, 611.3743286132812, 554.8255615234375, 693.3489379882812], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.73618698120117, 348.34197998046875, 211.83767700195312, 411.51922607421875], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [383.1363220214844, 349.2249755859375, 542.1131591796875, 410.7686767578125], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [216.76930236816406, 348.65301513671875, 375.7828674316406, 411.50933837890625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.54227066040039, 644.4090576171875, 544.9381713867188, 717.25146484375], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.1537971496582, 447.0932922363281, 282.8598937988281, 687.6914672851562], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.404762268066406, 135.83905029296875, 177.0564727783203, 180.99615478515625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [318.6331481933594, 432.9423828125, 534.73583984375, 701.1157836914062], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163330078125, 542.6654663085938, 279.3708190917969, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.42393493652344, 294.4362487792969, 280.23114013671875, 407.4449768066406], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.64824676513672, 160.73651123046875, 319.9102478027344, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.46868896484375, 327.7394714355469, 525.9568481445312, 429.54913330078125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.5950622558594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [66.79950714111328, 293.8616027832031, 528.5565185546875, 538.3836669921875], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [315.6535949707031, 489.1985168457031, 537.1474609375, 563.2764892578125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "table", "payload": null, "#-cols": 2, "#-rows": 1, "data": [[{"bbox": [384.03289794921875, 529.1906127929688, 390.0376892089844, 539.321044921875], "spans": [[0, 0]], "text": "3", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [451.9457092285156, 546.5225219726562, 457.95050048828125, 556.6529541015625], "spans": [[0, 1]], "text": "1", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "table", "payload": null, "#-cols": 6, "#-rows": 5, "data": [[{"bbox": [318.8807067871094, 345.5291748046875, 323.273193359375, 354.3141174316406], "spans": [[0, 0]], "text": "0", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [347.24871826171875, 345.5291748046875, 351.6412048339844, 354.3141174316406], "spans": [[0, 1], [0, 2]], "text": "1", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [347.24871826171875, 345.5291748046875, 351.6412048339844, 354.3141174316406], "spans": [[0, 1], [0, 2]], "text": "1", "type": "col_header", "col": 2, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [394.1042175292969, 344.2760009765625, 465.8810119628906, 354.4064025878906], "spans": [[0, 3], [0, 4]], 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{"bbox": [427.70159912109375, 525.3865966796875, 438.8056335449219, 530.4517822265625], "spans": [[4, 2]], "text": "(0.1)", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [468.5553283691406, 525.3865966796875, 482.0704345703125, 530.4517822265625], "spans": [[4, 3]], "text": "87.08", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [516.0186157226562, 525.3865966796875, 529.5337524414062, 530.4517822265625], "spans": [[4, 4]], "text": "81.14", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [306.11492919921875, 517.2933349609375, 356.2477111816406, 522.3585205078125], "spans": [[5, 0]], "text": "Canceled or forfeited", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [394.4322204589844, 516.6153564453125, 405.5362548828125, 521.6805419921875], "spans": [[5, 1]], "text": "(0. 1 )", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [431.02801513671875, 516.6153564453125, 436.4280090332031, 521.6805419921875], "spans": [[5, 2]], "text": "-", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [465.83099365234375, 516.6153564453125, 482.3501281738281, 521.6805419921875], "spans": [[5, 3]], "text": "102.01", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [516.0186157226562, 516.6153564453125, 529.5337524414062, 521.6805419921875], "spans": [[5, 4]], "text": "92.18", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [306.11492919921875, 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508.4490661621094, 534.0200805664062, 513.5142822265625], "spans": [[6, 4]], "text": "$ 104.51", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [84.0283203125, 577.606689453125, 239.16903686523438, 635.66650390625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [82.92005157470703, 500.71612548828125, 239.1903533935547, 558.2236938476562], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.94801330566406, 424.0904235839844, 239.17141723632812, 482.9522705078125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, 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Picture of a table:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 241.30950927734375, 126.94803619384766, 252.05723571777344], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "1. Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.111976623535156, 78.84822082519531, 286.3650817871094, 231.216796875], "page": 1, "span": [0, 712], "__ref_s3_data": null}], "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [50.111976623535156, 279.00335693359375, 286.3651123046875, 550.6049194335938], "page": 1, "span": [0, 1320], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [315.5670166015625, 458.7572021484375, 486.4019470214844, 478.3052062988281], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [315.5670166015625, 363.0691833496094, 491.1912536621094, 371.81719970703125], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "- c. Structure predicted by TableFormer:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [308.86199951171875, 232.7270965576172, 545.1151733398438, 277.4996337890625], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [308.86199951171875, 126.95307159423828, 545.1151733398438, 207.59063720703125], "page": 1, "span": [0, 363], "__ref_s3_data": null}], "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84806823730469, 545.1151123046875, 123.61963653564453], "page": 1, "span": [0, 229], "__ref_s3_data": null}], "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.9600715637207, 300.102294921875, 57.866634368896484], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.36505126953125, 716.7916259765625], "page": 2, "span": [0, 75], "__ref_s3_data": null}], "text": "considered as a solved problem, given enough ground-truth data to train on.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 563.9699096679688, 286.3651428222656, 692.4285888671875], "page": 2, "span": [0, 626], "__ref_s3_data": null}], "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 420.054931640625, 286.3651123046875, 560.4684448242188], "page": 2, "span": [0, 643], "__ref_s3_data": null}], "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 359.8269958496094, 286.3665771484375, 416.5534973144531], "page": 2, "span": [0, 242], "__ref_s3_data": null}], "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [61.56901550292969, 302.6770324707031, 286.3648986816406, 347.568115234375], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "- \u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.56901550292969, 245.0740509033203, 286.3648986816406, 289.9661560058594], "page": 2, "span": [0, 181], "__ref_s3_data": null}], "text": "- \u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 199.4270477294922, 286.36492919921875, 232.3631591796875], "page": 2, "span": [0, 106], "__ref_s3_data": null}], "text": "- \u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569007873535156, 153.779052734375, 286.3650817871094, 186.5966033935547], "page": 2, "span": [0, 131], "__ref_s3_data": null}], "text": "- \u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11200714111328, 96.63004302978516, 286.3651123046875, 141.401611328125], "page": 2, "span": [0, 231], "__ref_s3_data": null}], "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [60.97100067138672, 79.27845764160156, 183.7305450439453, 86.40372467041016], "page": 2, "span": [0, 40], "__ref_s3_data": null}], "text": "$^{1}$https://github.com/IBM/SynthTabNet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96015548706055, 300.102294921875, 57.86671829223633], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 683.9750366210938, 545.1151123046875, 716.7916259765625], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 659.5203247070312, 498.28021240234375, 670.26806640625], "page": 2, "span": [0, 37], "__ref_s3_data": null}], "text": "2. Previous work and State of the Art", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 461.54498291015625, 545.1151733398438, 649.7786254882812], "page": 2, "span": [0, 901], "__ref_s3_data": null}], "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 341.9270935058594, 545.115234375, 458.4305419921875], "page": 2, "span": [0, 552], "__ref_s3_data": null}], "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84815216064453, 545.1168823242188, 338.9322204589844], "page": 2, "span": [0, 1262], "__ref_s3_data": null}], "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 250.15101623535156, 716.7916259765625], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "tag-decoder which is constrained to the table-tags.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 516.5458984375, 286.3651428222656, 704.7806396484375], "page": 3, "span": [0, 864], "__ref_s3_data": null}], "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 301.297119140625, 286.3651123046875, 513.56103515625], "page": 3, "span": [0, 1007], "__ref_s3_data": null}], "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 169.733154296875, 286.36627197265625, 298.3112487792969], "page": 3, "span": [0, 619], "__ref_s3_data": null}], "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 145.30743408203125, 105.22545623779297, 156.05516052246094], "page": 3, "span": [0, 11], "__ref_s3_data": null}], "text": "3. Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 78.84813690185547, 286.3650817871094, 135.57470703125], "page": 3, "span": [0, 281], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.96023941040039, 300.102294921875, 57.86680221557617], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 503.3020935058594, 545.1151123046875, 524.1636352539062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.86199951171875, 465.6200866699219, 437.27001953125, 474.5266418457031], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 164.6382598876953, 545.1151733398438, 460.4686279296875], "page": 3, "span": [0, 1400], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 78.84823608398438, 545.1151123046875, 159.48580932617188], "page": 3, "span": [0, 406], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 695.9300537109375, 286.3651123046875, 716.7916259765625], "page": 4, "span": [0, 93], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 478.8949279785156, 286.3651428222656, 691.0396118164062], "page": 4, "span": [0, 983], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 357.50103759765625, 286.3651123046875, 474.0044860839844], "page": 4, "span": [0, 571], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 164.37611389160156, 286.3665466308594, 352.610595703125], "page": 4, "span": [0, 941], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84810638427734, 286.3651123046875, 159.4856719970703], "page": 4, "span": [0, 405], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209716796875, 48.96018600463867, 300.1022644042969, 57.86674880981445], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 567.6110229492188, 545.1150512695312, 624.338623046875], "page": 4, "span": [0, 267], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.86199951171875, 497.6080322265625, 545.1151733398438, 542.3795776367188], "page": 4, "span": [0, 210], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.8169860839844, 485.321044921875, 542.7439575195312, 494.22760009765625], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 460.0683288574219, 444.9360656738281, 470.8160400390625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 345.5131530761719, 545.115234375, 450.06060791015625], "page": 4, "span": [0, 504], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 324.45367431640625, 420.16058349609375, 334.30572509765625], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8619689941406, 127.00019073486328, 545.11572265625, 315.2347106933594], "page": 4, "span": [0, 907], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 78.84818267822266, 545.1151123046875, 123.73930358886719], "page": 4, "span": [0, 223], "__ref_s3_data": null}], "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199188232422, 567.0330810546875, 545.1084594726562, 588.0142211914062], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [50.11199951171875, 111.72905731201172, 286.365966796875, 264.2171936035156], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.86199951171875, 497.69305419921875, 545.1150512695312, 542.465576171875], "page": 5, "span": [0, 227], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 378.0381774902344, 545.1151123046875, 494.6601867675781], "page": 5, "span": [0, 563], "__ref_s3_data": null}], "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619689941406, 246.4272918701172, 545.1151123046875, 374.8857421875], "page": 5, "span": [0, 592], "__ref_s3_data": null}], "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 138.727294921875, 545.1151123046875, 243.39540100097656], "page": 5, "span": [0, 483], "__ref_s3_data": null}], "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8619384765625, 78.84827423095703, 545.1150512695312, 135.57484436035156], "page": 5, "span": [0, 286], "__ref_s3_data": null}], "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1209411621094, 48.96027755737305, 300.10223388671875, 57.86684036254883], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 636.1539916992188, 286.3651428222656, 716.7916259765625], "page": 6, "span": [0, 380], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 551.7369384765625, 286.3651123046875, 632.3755493164062], "page": 6, "span": [0, 371], "__ref_s3_data": null}], "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 347.76910400390625, 286.36572265625, 548.0780639648438], "page": 6, "span": [0, 985], "__ref_s3_data": null}], "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112022399902344, 323.12811279296875, 286.364990234375, 343.9896545410156], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as following:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [124.33001708984375, 274.92828369140625, 286.3624267578125, 298.71905517578125], "page": 6, "span": [0, 84], "__ref_s3_data": null}], "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112030029296875, 251.78411865234375, 281.596923828125, 261.4079895019531], "page": 6, "span": [0, 76], "__ref_s3_data": null}], "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11204528808594, 225.33538818359375, 171.9833526611328, 236.08311462402344], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. Experimental Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 205.8836212158203, 179.17501831054688, 215.7356719970703], "page": 6, "span": [0, 27], "__ref_s3_data": null}], "text": "5.1. Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11204528808594, 151.4931182861328, 286.36517333984375, 196.2656707763672], "page": 6, "span": [0, 207], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.66104888916016, 113.60411834716797, 286.3624572753906, 138.1719970703125], "page": 6, "span": [0, 77], "__ref_s3_data": null}], "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112060546875, 78.8481216430664, 286.3651428222656, 99.70968627929688], "page": 6, "span": [0, 117], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.12103271484375, 48.96010971069336, 300.1023254394531, 57.86667251586914], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.862060546875, 683.97509765625, 545.115234375, 716.7916870117188], "page": 6, "span": [0, 156], "__ref_s3_data": null}], "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 463.6259460449219, 545.1152954101562, 675.7706298828125], "page": 6, "span": [0, 1024], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 362.83001708984375, 545.1151733398438, 455.4224853515625], "page": 6, "span": [0, 419], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 238.12310791015625, 545.115234375, 354.6255798339844], "page": 6, "span": [0, 528], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 202.5936279296875, 397.44281005859375, 212.4456787109375], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 119.86811065673828, 545.1151733398438, 188.55067443847656], "page": 6, "span": [0, 299], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.8620300292969, 78.84710693359375, 545.115234375, 111.6646728515625], "page": 6, "span": [0, 155], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.74658203125, 167.89825439453125, 717.5986328125], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 653.8770141601562, 286.3651123046875, 698.6495971679688], "page": 7, "span": [0, 192], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.218994140625, 619.26123046875, 286.3623962402344, 641.6820068359375], "page": 7, "span": [0, 99], "__ref_s3_data": null}], "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11198425292969, 578.02099609375, 286.36285400390625, 610.9970092773438], "page": 7, "span": [0, 162], "__ref_s3_data": null}], "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 557.3284912109375, 170.45169067382812, 567.1805419921875], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. Quantitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 395.862060546875, 286.3651428222656, 548.35009765625], "page": 7, "span": [0, 723], "__ref_s3_data": null}], "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 178.705078125, 286.3651123046875, 199.56663513183594], "page": 7, "span": [0, 101], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [50.11199951171875, 166.7500762939453, 261.7873229980469, 175.65663146972656], "page": 7, "span": [0, 50], "__ref_s3_data": null}], "text": "FT: Model was trained on PubTabNet then finetuned.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11201477050781, 78.84806823730469, 286.3659973144531, 147.6501922607422], "page": 7, "span": [0, 346], "__ref_s3_data": null}], "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [295.1210021972656, 48.960079193115234, 300.102294921875, 57.866641998291016], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [308.86199951171875, 564.4229125976562, 545.1151733398438, 716.7916259765625], "page": 7, "span": [0, 737], "__ref_s3_data": null}], "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 454.68914794921875, 545.1151733398438, 475.5506896972656], "page": 7, "span": [0, 94], "__ref_s3_data": null}], "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [308.8619689941406, 271.8323059082031, 545.1156616210938, 424.3202819824219], "page": 7, "span": [0, 715], "__ref_s3_data": null}], "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 102.32206726074219, 545.1151733398438, 135.13864135742188], "page": 7, "span": [0, 148], "__ref_s3_data": null}], "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/5"}, {"prov": [{"bbox": [53.28603744506836, 705.4392700195312, 61.550289154052734, 713.3124389648438], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "- a.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [65.68241882324219, 705.4392700195312, 499.5556335449219, 713.3124389648438], "page": 8, "span": [0, 105], "__ref_s3_data": null}], "text": "- Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.81178283691406, 689.845703125, 284.3459167480469, 697.7188720703125], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.830810546875, 689.845703125, 431.0911865234375, 697.7188720703125], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [53.81178283691406, 575.8935546875, 385.93450927734375, 583.7667236328125], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"prov": [{"bbox": [380.42730712890625, 493.39715576171875, 549.4217529296875, 499.69573974609375], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"prov": [{"bbox": [50.11199951171875, 426.3501281738281, 545.11376953125, 471.1226501464844], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [62.595001220703125, 324.3650817871094, 532.6304931640625, 333.2716369628906], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [50.11199951171875, 290.7525939941406, 163.75579833984375, 300.6046447753906], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. Qualitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84805297851562, 286.3651123046875, 255.1266326904297], "page": 8, "span": [0, 866], "__ref_s3_data": null}], "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 290.5433654785156, 460.8484802246094, 301.29107666015625], "page": 8, "span": [0, 27], "__ref_s3_data": null}], "text": "6. Future Work & Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.86199951171875, 138.69407653808594, 545.1151733398438, 279.10662841796875], "page": 8, "span": [0, 640], "__ref_s3_data": null}], "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 109.15335845947266, 364.4058532714844, 119.90107727050781], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [313.3450012207031, 79.06324768066406, 545.1134033203125, 98.0382080078125], "page": 8, "span": [0, 121], "__ref_s3_data": null}], "text": "- [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [132.8419952392578, 656.4699096679688, 465.37591552734375, 681.4251098632812], "page": 11, "span": [0, 83], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 620.0913696289062, 175.96437072753906, 630.839111328125], "page": 11, "span": [0, 26], "__ref_s3_data": null}], "text": "1. Details on the datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 601.1686401367188, 150.364013671875, 611.0206909179688], "page": 11, "span": [0, 21], "__ref_s3_data": null}], "text": "1.1. Data preparation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 403.8451843261719, 286.3651428222656, 592.0797119140625], "page": 11, "span": [0, 931], "__ref_s3_data": null}], "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 164.54029846191406, 286.3651123046875, 400.5947265625], "page": 11, "span": [0, 1149], "__ref_s3_data": null}], "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 140.42730712890625, 286.3649597167969, 161.28985595703125], "page": 11, "span": [0, 92], "__ref_s3_data": null}], "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11198425292969, 119.7578125, 153.60784912109375, 129.60986328125], "page": 11, "span": [0, 23], "__ref_s3_data": null}], "text": "1.2. Synthetic datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.11198425292969, 77.852294921875, 286.36505126953125, 110.66886901855469], "page": 11, "span": [0, 167], "__ref_s3_data": null}], "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 584.572265625, 545.1151123046875, 629.3448486328125], "page": 11, "span": [0, 221], "__ref_s3_data": null}], "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 559.9032592773438, 545.1150512695312, 580.7648315429688], "page": 11, "span": [0, 89], "__ref_s3_data": null}], "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 475.45721435546875, 545.1151123046875, 556.0947875976562], "page": 11, "span": [0, 373], "__ref_s3_data": null}], "text": "- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 343.19134521484375, 545.1151733398438, 471.6497802734375], "page": 11, "span": [0, 573], "__ref_s3_data": null}], "text": "- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 294.61138916015625, 545.1151733398438, 339.3839111328125], "page": 11, "span": [0, 195], "__ref_s3_data": null}], "text": "- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 246.0314178466797, 545.1152954101562, 290.803955078125], "page": 11, "span": [0, 218], "__ref_s3_data": null}], "text": "- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 185.4964141845703, 545.1151733398438, 242.22396850585938], "page": 11, "span": [0, 238], "__ref_s3_data": null}], "text": "- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 145.01368713378906, 545.1087646484375, 169.70941162109375], "page": 11, "span": [0, 47], "__ref_s3_data": null}], "text": "2. Prediction post-processing for PDF documents", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.8620300292969, 77.85139465332031, 545.1151733398438, 134.57896423339844], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [292.63104248046875, 48.96039962768555, 302.5936279296875, 57.86696243286133], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 605.6360473632812, 545.1137084960938, 626.4976196289062], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [61.569000244140625, 560.20703125, 286.3651123046875, 581.068603515625], "page": 12, "span": [0, 61], "__ref_s3_data": null}], "text": "- \u00b7 TableFormer output does not include the table cell content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569000244140625, 527.0670166015625, 286.3651428222656, 547.9285888671875], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "- \u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 396.2931213378906, 286.3651123046875, 512.7965698242188], "page": 12, "span": [0, 545], "__ref_s3_data": null}], "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 372.068115234375, 286.3649597167969, 392.9306640625], "page": 12, "span": [0, 68], "__ref_s3_data": null}], "text": "Here is a step-by-step description of the prediction postprocessing:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 335.8881530761719, 286.3650817871094, 368.7046813964844], "page": 12, "span": [0, 173], "__ref_s3_data": null}], "text": "- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 287.7532043457031, 286.36505126953125, 332.52471923828125], "page": 12, "span": [0, 187], "__ref_s3_data": null}], "text": "- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 263.5272216796875, 286.36492919921875, 284.3897705078125], "page": 12, "span": [0, 97], "__ref_s3_data": null}], "text": "- 3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 227.34722900390625, 286.3651123046875, 260.164794921875], "page": 12, "span": [0, 131], "__ref_s3_data": null}], "text": "- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.11199951171875, 191.16722106933594, 286.3650817871094, 223.98377990722656], "page": 12, "span": [0, 169], "__ref_s3_data": null}], "text": "- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [110.70498657226562, 137.89439392089844, 286.3623962402344, 168.5640869140625], "page": 12, "span": [0, 81], "__ref_s3_data": null}], "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.11199951171875, 103.07321166992188, 286.36199951171875, 124.6520767211914], "page": 12, "span": [0, 103], "__ref_s3_data": null}], "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 78.84821319580078, 286.3649597167969, 99.70977783203125], "page": 12, "span": [0, 110], "__ref_s3_data": null}], "text": "- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.86199951171875, 536.2962036132812, 545.1151733398438, 581.0687866210938], "page": 12, "span": [0, 183], "__ref_s3_data": null}], "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.86199951171875, 512.0361938476562, 545.114990234375, 532.8977661132812], "page": 12, "span": [0, 91], "__ref_s3_data": null}], "text": "- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 404.08929443359375, 545.1151123046875, 508.6367492675781], "page": 12, "span": [0, 471], "__ref_s3_data": null}], "text": "- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 332.00836181640625, 545.1151733398438, 400.6898498535156], "page": 12, "span": [0, 311], "__ref_s3_data": null}], "text": "- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 224.06141662597656, 545.1151733398438, 328.6089172363281], "page": 12, "span": [0, 503], "__ref_s3_data": null}], "text": "- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.8620300292969, 187.8454132080078, 545.1168823242188, 220.66197204589844], "page": 12, "span": [0, 113], "__ref_s3_data": null}], "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862060546875, 163.58441162109375, 545.1150512695312, 184.44696044921875], "page": 12, "span": [0, 101], "__ref_s3_data": null}], "text": "- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 127.3694076538086, 545.1150512695312, 160.18597412109375], "page": 12, "span": [0, 117], "__ref_s3_data": null}], "text": "- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 103.10841369628906, 545.114990234375, 123.969970703125], "page": 12, "span": [0, 107], "__ref_s3_data": null}], "text": "- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862060546875, 78.84840393066406, 545.1151733398438, 99.70997619628906], "page": 12, "span": [0, 118], "__ref_s3_data": null}], "text": "- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [292.6310729980469, 48.96040725708008, 302.5936584472656, 57.86697006225586], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [50.11199951171875, 707.8850708007812, 88.84658813476562, 716.7916259765625], "page": 13, "span": [0, 10], "__ref_s3_data": null}], "text": "phan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 683.9750366210938, 286.3649597167969, 704.8366088867188], "page": 13, "span": [0, 76], "__ref_s3_data": null}], "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.11199951171875, 660.2941284179688, 286.364990234375, 680.8369140625], "page": 13, "span": [0, 97], "__ref_s3_data": null}], "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/8"}, {"name": "Table", "type": "table", "$ref": "#/tables/9"}, {"name": "Table", "type": "table", "$ref": "#/tables/10"}, {"prov": [{"bbox": [63.340999603271484, 281.0370788574219, 273.1334228515625, 289.9436340332031], "page": 13, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/11"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/12"}, {"name": "Table", "type": "table", "$ref": "#/tables/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/14"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [308.86199951171875, 464.54010009765625, 545.1151123046875, 485.4016418457031], "page": 13, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/15"}, {"name": "Table", "type": "table", "$ref": "#/tables/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/18"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [312.3429870605469, 102.60006713867188, 541.63232421875, 111.50663757324219], "page": 13, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/19"}, {"name": "Table", "type": "table", "$ref": "#/tables/20"}, {"prov": [{"bbox": [50.11199951171875, 414.36810302734375, 286.3650817871094, 435.2296447753906], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"name": "Table", "type": "table", "$ref": "#/tables/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/23"}, {"prov": [{"bbox": [54.61899948120117, 111.27507781982422, 281.85589599609375, 120.181640625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"prov": [{"bbox": [292.6309814453125, 48.96007537841797, 302.59356689453125, 57.86663818359375], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/24"}, {"name": "Table", "type": "table", "$ref": "#/tables/25"}, {"name": "Table", "type": "table", "$ref": "#/tables/26"}, {"prov": [{"bbox": [315.7900085449219, 411.4090881347656, 538.1852416992188, 420.3156433105469], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/27"}, {"name": "Table", "type": "table", "$ref": "#/tables/28"}, {"name": "Table", "type": "table", "$ref": "#/tables/29"}, {"prov": [{"bbox": [344.9849853515625, 99.54707336425781, 508.9893493652344, 108.45364379882812], "page": 14, "span": [0, 40], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/30"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/31"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/18"}, {"name": "Table", "type": "table", "$ref": "#/tables/32"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/19"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/20"}, {"prov": [{"bbox": [84.23300170898438, 138.7420654296875, 252.24224853515625, 147.64862060546875], "page": 15, "span": [0, 41], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/33"}, {"prov": [{"bbox": [292.6309814453125, 48.9600944519043, 302.59356689453125, 57.86665725708008], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/34"}, {"name": "Table", "type": "table", "$ref": "#/tables/35"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/36"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/22"}, {"prov": [{"bbox": [308.8619689941406, 118.20308685302734, 545.1151123046875, 139.0646514892578], "page": 15, "span": [0, 106], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/37"}, {"prov": [{"bbox": [50.11199951171875, 262.80108642578125, 545.1138305664062, 283.6626281738281], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/23"}, {"prov": [{"bbox": [292.6309814453125, 48.960079193115234, 302.59356689453125, 57.866641998291016], "page": 16, "span": [0, 2], "__ref_s3_data": null}], "text": "16", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [315.65362548828125, 489.19854736328125, 537.1475219726562, 563.2765502929688], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [314.78173828125, 381.9505615234375, 539.1802978515625, 453.9347229003906], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [312.10369873046875, 541.39013671875, 550.38916015625, 713.5591430664062], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [74.30525970458984, 608.2984619140625, 519.9801025390625, 714.0887451171875], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.03325653076172, 284.3311462402344, 285.3731689453125, 534.3346557617188], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [49.97503662109375, 604.4210815429688, 301.6335754394531, 688.287353515625], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [305.58367919921875, 611.3732299804688, 554.8258666992188, 693.3458251953125], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.736167907714844, 348.3419189453125, 211.83778381347656, 411.51934814453125], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [383.1364440917969, 349.2250671386719, 542.1132202148438, 410.7687072753906], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [216.76925659179688, 348.6529541015625, 375.7829284667969, 411.5093994140625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.54228973388672, 644.4091186523438, 544.938232421875, 717.25146484375], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.1537971496582, 447.0933532714844, 282.8598937988281, 687.6914672851562], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.404788970947266, 135.83905029296875, 177.05642700195312, 180.99615478515625], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [318.6332092285156, 432.9424133300781, 534.73583984375, 701.1157836914062], "page": 14, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 13: Table predictions example on colorful table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163444519043, 542.66552734375, 279.370849609375, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [55.423954010009766, 294.436279296875, 280.2310791015625, 407.4449462890625], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [50.64816665649414, 160.73651123046875, 319.91033935546875, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.4686279296875, 327.739501953125, 525.9568481445312, 429.5491638183594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.594970703125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [66.79946899414062, 293.8616027832031, 528.5564575195312, 538.3836669921875], "page": 16, "span": [0, 153], "__ref_s3_data": null}], "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [315.65362548828125, 489.19854736328125, 537.1475219726562, 563.2765502929688], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "table", "payload": null, "#-cols": 2, "#-rows": 1, "data": [[{"bbox": [384.03289794921875, 529.1906127929688, 390.0376892089844, 539.321044921875], "spans": [[0, 0]], "text": "3", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [451.9457092285156, 546.5225219726562, 457.95050048828125, 556.6529541015625], "spans": [[0, 1]], "text": "1", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "table", "payload": null, "#-cols": 6, "#-rows": 5, "data": [[{"bbox": [318.8807067871094, 345.5291748046875, 323.273193359375, 354.3141174316406], "spans": [[0, 0]], "text": "0", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [347.24871826171875, 345.5291748046875, 351.6412048339844, 354.3141174316406], "spans": [[0, 1], [0, 2]], "text": "1", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [347.24871826171875, 345.5291748046875, 351.6412048339844, 354.3141174316406], "spans": [[0, 1], [0, 2]], "text": "1", "type": "col_header", "col": 2, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [394.1042175292969, 344.2760009765625, 465.8810119628906, 354.4064025878906], "spans": [[0, 3], [0, 4]], 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[3, 5], [4, 5]], "text": "2", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 4, "row-header": false, "row-span": [2, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [310.67584228515625, 636.7794799804688, 542.9547119140625, 718.80615234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. 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"row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [281.4774169921875, 502.1754150390625, 289.228759765625, 506.5333251953125], "spans": [[9, 5]], "text": "955", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 9, "row-header": false, "row-span": [9, 10]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [304.9219970703125, 504.09930419921875, 550.2321166992188, 573.485107421875], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "table", "payload": null, "#-cols": 5, "#-rows": 7, "data": [[{"bbox": null, "spans": [[0, 0]], "text": "", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [392.0967102050781, 565.3603515625, 438.0144958496094, 570.425537109375], "spans": [[0, 1], [0, 2]], "text": "Shares (in millions)", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [392.0967102050781, 565.3603515625, 438.0144958496094, 570.425537109375], "spans": [[0, 1], [0, 2]], "text": "Shares (in millions)", "type": "col_header", "col": 2, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [459.0486145019531, 559.1006469726562, 542.0001831054688, 570.3758544921875], "spans": [[0, 3], [0, 4]], "text": "Weighted Average Grant Date Fair Value", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [459.0486145019531, 559.1006469726562, 542.0001831054688, 570.3758544921875], "spans": [[0, 3], [0, 4]], "text": "Weighted Average Grant Date Fair Value", "type": "col_header", "col": 4, "col-header": true, "col-span": [3, 5], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": null, "spans": [[1, 0]], "text": "", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [393.24420166015625, 550.1876831054688, 407.3463134765625, 555.2528686523438], "spans": [[1, 1]], "text": "RS U s", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [427.1832275390625, 550.1876831054688, 440.98779296875, 555.2528686523438], "spans": [[1, 2]], "text": "PSUs", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [468.3825378417969, 550.1876831054688, 482.4846496582031, 555.2528686523438], "spans": [[1, 3]], "text": "RSUs", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [516.92578125, 550.1876831054688, 530.7303466796875, 555.2528686523438], "spans": [[1, 4]], "text": "PSUs", "type": "col_header", "col": 4, "col-header": true, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [306.11492919921875, 542.323974609375, 364.65606689453125, 547.38916015625], "spans": [[2, 0]], "text": "Nonvested on Janua ry 1", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [396.2466125488281, 542.0215454101562, 403.75531005859375, 547.0867309570312], "spans": [[2, 1]], "text": "1. 1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [429.8183898925781, 542.0215454101562, 437.32708740234375, 547.0867309570312], "spans": [[2, 2]], "text": "0.3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [465.5285949707031, 542.0215454101562, 483.5500183105469, 547.0867309570312], "spans": [[2, 3]], "text": "90.10 $", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [513.4482421875, 542.0215454101562, 531.4696655273438, 547.0867309570312], "spans": [[2, 4]], "text": "$ 91.19", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [306.11492919921875, 533.2503051757812, 325.6267395019531, 538.3154907226562], "spans": [[3, 0]], "text": "Granted", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [396.2466125488281, 533.2503051757812, 403.75531005859375, 538.3154907226562], "spans": [[3, 1]], "text": "0. 5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [429.8183898925781, 533.2503051757812, 437.32708740234375, 538.3154907226562], "spans": [[3, 2]], "text": "0.1", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [466.435791015625, 533.2503051757812, 482.5483093261719, 538.3154907226562], "spans": [[3, 3]], "text": "117.44", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [514.2906494140625, 533.2503051757812, 530.809814453125, 538.3154907226562], "spans": [[3, 4]], "text": "122.41", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [306.11492919921875, 525.3865966796875, 322.628662109375, 530.4517822265625], "spans": [[4, 0]], "text": "Vested", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [394.4322204589844, 525.3865966796875, 405.5362548828125, 530.4517822265625], "spans": [[4, 1]], "text": "(0. 5 )", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [427.70159912109375, 525.3865966796875, 438.8056335449219, 530.4517822265625], "spans": [[4, 2]], "text": "(0.1)", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [468.5553283691406, 525.3865966796875, 482.0704345703125, 530.4517822265625], "spans": [[4, 3]], "text": "87.08", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [516.0186157226562, 525.3865966796875, 529.5337524414062, 530.4517822265625], "spans": [[4, 4]], "text": "81.14", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [306.11492919921875, 517.2933349609375, 356.2477111816406, 522.3585205078125], "spans": [[5, 0]], "text": "Canceled or forfeited", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [394.4322204589844, 516.6153564453125, 405.5362548828125, 521.6805419921875], "spans": [[5, 1]], "text": "(0. 1 )", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [431.02801513671875, 516.6153564453125, 436.4280090332031, 521.6805419921875], "spans": [[5, 2]], "text": "-", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [465.83099365234375, 516.6153564453125, 482.3501281738281, 521.6805419921875], "spans": [[5, 3]], "text": "102.01", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [516.0186157226562, 516.6153564453125, 529.5337524414062, 521.6805419921875], "spans": [[5, 4]], "text": "92.18", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [306.11492919921875, 508.4490661621094, 373.3576354980469, 513.5142822265625], "spans": [[6, 0]], "text": "Nonvested on December 31", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [396.2466125488281, 508.4490661621094, 403.75531005859375, 513.5142822265625], "spans": [[6, 1]], "text": "1.0", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [429.5159912109375, 508.4490661621094, 437.0246887207031, 513.5142822265625], "spans": [[6, 2]], "text": "0.3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [463.7142028808594, 508.4490661621094, 484.7396545410156, 513.5142822265625], "spans": [[6, 3]], "text": "104.85 $", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [512.99462890625, 508.4490661621094, 534.0200805664062, 513.5142822265625], "spans": [[6, 4]], "text": "$ 104.51", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [84.0283203125, 577.606689453125, 239.1690673828125, 635.6664428710938], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [82.92001342773438, 500.716064453125, 239.1903533935547, 558.2236938476562], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.94786071777344, 424.0904235839844, 239.17135620117188, 482.9522705078125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.31758117675781, 304.7430114746094, 248.87306213378906, 395.9864501953125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [310.3294372558594, 655.8525390625, 555.8338623046875, 690.8223876953125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9566345214844, 607.2774658203125, 555.7466430664062, 637.385498046875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9635314941406, 558.448486328125, 555.7054443359375, 596.2946166992188], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946044921875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2694091796875, 354.97760009765625, 490.08184814453125, 403.53253173828125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [334.9334411621094, 289.2789001464844, 490.0914611816406, 338.0523681640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2545471191406, 224.31207275390625, 490.22369384765625, 272.92431640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.72642135620117, 447.7555236816406, 283.114013671875, 518.3907470703125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.43488693237305, 300.17974853515625, 310.7267150878906, 338.51251220703125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [50.86823654174805, 249.55401611328125, 310.6080017089844, 287.90374755859375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.27280807495117, 200.086669921875, 311.0897216796875, 238.271484375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [318.98101806640625, 577.3739013671875, 534.6228637695312, 630.765380859375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.0057678222656, 512.142333984375, 534.408935546875, 565.8936767578125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [328.13812255859375, 433.7275695800781, 523.8915405273438, 503.3182067871094], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.4707946777344, 314.05645751953125, 518.5693359375, 361.09698486328125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.982666015625, 256.30419921875, 519.0963745117188, 302.7562561035156], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.82879638671875, 198.8935546875, 519.6065673828125, 245.5906982421875], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.0649719238281, 122.80792236328125, 533.7738647460938, 182.1591796875], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [55.1163444519043, 542.66552734375, 279.370849609375, 655.7449951171875], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [54.28135299682617, 418.4728698730469, 279.2568359375, 531.7384033203125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [50.64816665649414, 160.73651123046875, 319.91033935546875, 286.0196838378906], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.0059814453125, 569.088623046875, 525.95166015625, 670.452880859375], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.384765625, 447.9079284667969, 526.1268920898438, 550.027099609375], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [323.4686279296875, 327.739501953125, 525.9568481445312, 429.5491638183594], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [353.6920471191406, 156.22674560546875, 495.4288024902344, 304.594970703125], "page": 15, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 792.0, "page": 2, "width": 612.0}, {"height": 792.0, "page": 3, "width": 612.0}, {"height": 792.0, "page": 4, "width": 612.0}, {"height": 792.0, "page": 5, "width": 612.0}, {"height": 792.0, "page": 6, "width": 612.0}, {"height": 792.0, "page": 7, "width": 612.0}, {"height": 792.0, "page": 8, "width": 612.0}, {"height": 792.0, "page": 9, "width": 612.0}, {"height": 792.0, "page": 10, "width": 612.0}, {"height": 792.0, "page": 11, "width": 612.0}, {"height": 792.0, "page": 12, "width": 612.0}, {"height": 792.0, "page": 13, "width": 612.0}, {"height": 792.0, "page": 14, "width": 612.0}, {"height": 792.0, "page": 15, "width": 612.0}, {"height": 792.0, "page": 16, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
index b58d1e90..1d446e1f 100644
--- a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
+++ b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", 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{"l": 451.9457100000001, "t": 235.34704999999997, "r": 457.95050000000003, "b": 245.47748, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "3", "bbox": {"l": 385.09399, "t": 357.76030999999995, "r": 391.09879, "b": 367.89072, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3", "bbox": {"l": 366.70102, "t": 449.12082, "r": 372.70581, "b": 459.25122, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 331.19681, "t": 269.35266, "r": 337.2016, "b": 279.48308999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2", "bbox": {"l": 333.43451, "t": 380.7265, "r": 339.4393, "b": 390.85689999999994, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "2", "bbox": {"l": 331.90424, "t": 473.32291, "r": 337.90903, "b": 483.45331, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "1", "bbox": {"l": 478.07210999999995, "t": 341.0368000000001, "r": 484.0769, "b": 351.16720999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "1", "bbox": {"l": 459.87621999999993, "t": 437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868066072463989, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.75862056016922, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.48547354340553284, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.909633457660675, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671402931213, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882684707642, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": 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Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. 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In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501362085342407, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78167724609375, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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"caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214613437652588, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501362085342407, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. 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Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045888543128967, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773135542869568, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 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"r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.1036071777344, "t": 78.44092559814453, "r": 550.3890380859375, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746916890144348, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, 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The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.1036071777344, "t": 78.44092559814453, "r": 550.3890380859375, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746916890144348, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 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the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879810571670532, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. 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"bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 267.83636, "r": 545.11511, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9667503833770752, "cells": [{"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879810571670532, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 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109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": 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The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": 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417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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{"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466321468353271, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. 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(**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466321468353271, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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"coord_origin": "TOPLEFT"}}, {"id": 30, "text": "provide full description of", "bbox": {"l": 431.1738, "t": 157.80517999999995, "r": 498.87753000000004, "b": 163.54578000000004, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the table structure", "bbox": {"l": 440.5289, "t": 163.92516999999998, "r": 489.51827999999995, "b": 169.66576999999995, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Structure Tags", "bbox": {"l": 328.37479, "t": 178.25385000000006, "r": 367.72333, "b": 183.99445000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "BBoxes in sync", "bbox": {"l": 331.84451, "t": 123.90886999999998, "r": 373.67963, "b": 129.64948000000015, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "with tag sequence", "bbox": {"l": 331.84451, "t": 129.00885000000017, "r": 381.17786, "b": 134.74945000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Encoder", "bbox": {"l": 196.62633, "t": 88.11621000000002, "r": 219.42332, "b": 93.85681, "coord_origin": "TOPLEFT"}}, {"id": 36, 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a 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attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30531311035156, "t": 77.91128540039062, "r": 519.980224609375, "b": 183.70115661621094, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Names", "bbox": {"l": 82.140205, "t": 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. 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The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. ResNet by default downsamples the", "bbox": {"l": 308.862, "t": 261.49042, "r": 517.55847, "b": 270.39697, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "image", "bbox": {"l": 520.76642, "t": 261.49042, "r": 545.11499, "b": 270.39697, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "resolution by 32 and then the encoded image is provided", "bbox": {"l": 308.862, "t": 273.44537, "r": 534.80377, "b": 282.35196, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "to", "bbox": {"l": 537.36414, "t": 273.44537, "r": 545.11505, "b": 282.35196, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "both the", "bbox": {"l": 308.862, "t": 285.40039, "r": 341.24045, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Structure Decoder", "bbox": {"l": 343.73099, "t": 285.49005, "r": 417.23508, "b": 294.07782000000003, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": ", and", "bbox": {"l": 417.23398, "t": 285.40039, "r": 436.60129, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "Cell BBox Decoder", "bbox": {"l": 439.09198, "t": 285.49005, "r": 516.56116, "b": 294.07782000000003, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": ".", "bbox": {"l": 516.56097, "t": 285.40039, "r": 519.05164, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719565868377686, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30531311035156, "t": 77.91128540039062, "r": 519.980224609375, "b": 183.70115661621094, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. ResNet by default downsamples the", "bbox": {"l": 308.862, "t": 261.49042, "r": 517.55847, "b": 270.39697, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "image", "bbox": {"l": 520.76642, "t": 261.49042, "r": 545.11499, "b": 270.39697, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "resolution by 32 and then the encoded image is provided", "bbox": {"l": 308.862, "t": 273.44537, "r": 534.80377, "b": 282.35196, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "to", "bbox": {"l": 537.36414, "t": 273.44537, "r": 545.11505, "b": 282.35196, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "both the", "bbox": {"l": 308.862, "t": 285.40039, "r": 341.24045, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "Structure Decoder", "bbox": {"l": 343.73099, "t": 285.49005, "r": 417.23508, "b": 294.07782000000003, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": ", and", "bbox": {"l": 417.23398, "t": 285.40039, "r": 436.60129, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "Cell BBox Decoder", "bbox": {"l": 439.09198, "t": 285.49005, "r": 516.56116, "b": 294.07782000000003, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": ".", "bbox": {"l": 516.56097, "t": 285.40039, "r": 519.05164, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719565868377686, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 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"text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363928437232971, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363928437232971, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554281830787659, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1356506347656, "r": 283.0443420410156, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": 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286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209139466285706, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433356404304504, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": 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FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1356506347656, "r": 283.0443420410156, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 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"text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 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"FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, 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"coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209139466285706, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433356404304504, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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"bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775566458702087, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787977695465088, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554281830787659, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1356506347656, "r": 283.0443420410156, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": {"l": 82.165001, "t": 455.58035, "r": 101.5323, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PTN", "bbox": {"l": 134.86716, "t": 455.58035, "r": 153.68651, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "-", "bbox": {"l": 183.62411, "t": 455.58035, "r": 186.94167, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "-", "bbox": {"l": 227.88795000000002, "t": 455.58035, "r": 231.20551, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "93.01", "bbox": {"l": 259.69855, "t": 455.58035, "r": 282.11441, "b": 464.48691, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 468.13336, "r": 117.38329000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PTN", "bbox": {"l": 134.86766, "t": 468.13336, "r": 153.68701, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "98.5", "bbox": {"l": 176.57111, "t": 468.13336, "r": 194.00566, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 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"FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, 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3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "89.6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "TableFormer", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209139466285706, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433356404304504, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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"bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}], "children": [{"id": 73, "label": "text", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775566458702087, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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283.55092999999994, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "104.85 $", "bbox": {"l": 463.7142, "t": 278.48572, "r": 484.73965000000004, "b": 283.55092999999994, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "$ 104.51", "bbox": {"l": 512.99463, "t": 278.48572, "r": 534.02008, "b": 283.55092999999994, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Figure 5:", "bbox": {"l": 50.112, "t": 320.87735, "r": 86.864021, "b": 329.78391, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration", "bbox": {"l": 93.917542, "t": 320.87735, "r": 545.11371, "b": 329.78391, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "demonstrates TableFormer predictions on previously unseen language (Japanese). 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"TOPLEFT"}}, {"id": 180, "text": "Predicted Structure", "bbox": {"l": 384.35437, "t": 381.77722, "r": 430.99261, "b": 386.44281, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. 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"label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153726696968079, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442477226257324, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318730592727661, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442477226257324, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318730592727661, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In Andrea Vedaldi, Horst Bischof, Thomas", "bbox": {"l": 328.78101, "t": 75.88347999999996, "r": 545.11346, "b": 83.89948000000015, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "Brox, and Jan-Michael Frahm, editors,", "bbox": {"l": 328.78101, "t": 86.84149000000002, "r": 472.30618, "b": 94.85748000000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Computer Vision -", "bbox": {"l": 475.88501, "t": 86.92218000000003, "r": 545.11456, "b": 94.65125, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "ECCV 2020", "bbox": {"l": 328.78101, "t": 97.88116000000002, "r": 371.92734, "b": 105.61023, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": ", pages 564-580, Cham, 2020. 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A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Complex", "bbox": {"l": 287.89441, "t": 141.71063000000004, "r": 310.14572, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Non", "bbox": {"l": 289.23572, "t": 93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 147.5909423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.6033807992935181, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, 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[{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 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713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 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"Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, 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"b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 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"bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260513186454773, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 147.5909423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.6033807992935181, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": 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"TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260513186454773, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. 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+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 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437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868061304092407, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586213946342468, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.48547235131263733, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096333980560303, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671998977661, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501352548599243, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "picture", "bbox": {"l": 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501352548599243, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742762207984924, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214621186256409, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}], "body": [{"label": "section_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868061304092407, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers."}, {"label": "section_header", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586213946342468, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research"}, {"label": "key_value_region", "id": 25, "page_no": 0, "cluster": {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.48547235131263733, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096333980560303, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671998977661, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549326539039612, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501352548599243, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742762207984924, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214621186256409, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9791521430015564, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045889139175415, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773146271705627, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.98029625415802, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.974253237247467, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953676223754883, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.977432370185852, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428722262382507, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871949553489685, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869041442871094, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.877809464931488, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.98029625415802, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.974253237247467, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953676223754883, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.977432370185852, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428722262382507, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871949553489685, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869041442871094, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.877809464931488, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.98029625415802, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.974253237247467, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953676223754883, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.977432370185852, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428722262382507, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871949553489685, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869041442871094, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.877809464931488, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0", "bbox": {"l": 316.57742, "t": 152.47400000000005, "r": 320.3934, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "7", "bbox": {"l": 312.19775, "t": 120.57050000000004, "r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": 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table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44087219238281, "r": 550.38916015625, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 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The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767215609550476, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822595119476318, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. 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the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879812359809875, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840090274810791, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.87156081199646, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.961143434047699, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19387817382812, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 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While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376963376998901, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565719723701477, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19387817382812, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": 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"children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19387817382812, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, 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"b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9667726755142212, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9789542555809021, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9043694138526917, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466594457626343, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376963376998901, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565719723701477, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.961143434047699, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19387817382812, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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"b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9667726755142212, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9789542555809021, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9043694138526917, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466594457626343, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376963376998901, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565719723701477, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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The bounding boxes grabs the content from the PDF and inserts it in the structure.", "bbox": {"l": 50.111992, "t": 216.06035999999995, "r": 436.0134, "b": 224.96691999999996, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Input Image", "bbox": {"l": 74.253464, "t": 258.21472000000006, "r": 101.75846, "b": 264.17474000000004, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tokenised Tags", "bbox": {"l": 122.29972, "t": 258.34520999999995, "r": 157.83972, "b": 264.30524, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Multi-Head Attention", "bbox": {"l": 78.549347, "t": 371.38579999999996, "r": 125.68359000000001, "b": 377.04782, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Add", "bbox": {"l": 78.513298, "t": 391.31857, "r": 84.644547, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "& Normalisation", "bbox": {"l": 116.52705, "t": 391.31857, "r": 125.11079999999998, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Feed Forward Network", "bbox": 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30525970458984, "t": 77.9112319946289, "r": 519.9801025390625, "b": 183.70150756835938, "coord_origin": "TOPLEFT"}, "confidence": 0.9296805262565613, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Names", "bbox": {"l": 82.140205, "t": 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851908087730408, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851908087730408, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719562292098999, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423347115516663, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441831469535828, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880596995353699, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": 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"TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.955427348613739, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500411748886108, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1357727050781, "r": 283.04437255859375, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": 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We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713196754455566, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.4068908691406, "t": 247.87631225585938, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691731929779053, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500411748886108, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.36848831176758, "t": 409.1357727050781, "r": 283.04437255859375, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209174633026123, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433308124542236, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713196754455566, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4068908691406, "t": 247.87631225585938, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691731929779053, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775568842887878, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787970542907715, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.955427348613739, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500411748886108, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209174633026123, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433308124542236, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713196754455566, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4068908691406, "t": 247.87631225585938, "r": 533.6420288085938, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691731929779053, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775568842887878, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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"TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986502170562744, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": 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different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.943652331829071, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. 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"label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153599143028259, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, 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Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852360486984253, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.943652331829071, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470669746399, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318749070167542, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8709856867790222, "cells": [{"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.58367919921875, "t": 98.65414428710938, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699168920516968, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.57891845703125, "coord_origin": "TOPLEFT"}, "confidence": 0.787392258644104, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986502170562744, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452993154525757, "cells": [{"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46292340755462646, "cells": [{"id": 3, "text": "Example table from FinTabNet:", "bbox": {"l": 304.83081, "t": 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"label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153599143028259, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852360486984253, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.943652331829071, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470669746399, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318749070167542, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069585204124451, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497079730033875, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335852265357971, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.978333592414856, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596062302589417, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699996113777161, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568929672241211, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986662030220032, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Complex", "bbox": {"l": 287.89441, "t": 141.71063000000004, "r": 310.14572, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Non", "bbox": {"l": 289.23572, "t": 93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, 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[{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 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713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 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"Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, 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"b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 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"bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54228973388672, "t": 74.74851989746094, "r": 544.938232421875, "b": 147.5908966064453, "coord_origin": "TOPLEFT"}, "confidence": 0.6033812761306763, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": 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"TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.96295565366745, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767084717750549, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347665309906006, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280701637268066, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491711854934692, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587164521217346, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298409819602966, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510802268982, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942293524742126, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423391819000244, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562006592750549, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126085042953491, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459475636482239, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716898918151855, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584902882575989, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.759074866771698, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126599431037903, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. 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diff --git a/tests/data/groundtruth/docling_v1/2206.01062.json b/tests/data/groundtruth/docling_v1/2206.01062.json
index e7a6c97f..88d92f64 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.json
+++ b/tests/data/groundtruth/docling_v1/2206.01062.json
@@ -1 +1 @@
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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [336.6930236816406, 553.3746948242188, 439.457275390625, 599.942626953125], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79803466796875, 533.9879760742188, 111.94354248046875, 544.297119140625], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "ABSTRACT", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 257.7068176269531, 295.5601806640625, 529.095458984375], "page": 1, "span": [0, 1595], "__ref_s3_data": null}], "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 230.69398498535156, 134.81988525390625, 241.00308227539062], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "CCS CONCEPTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.79798889160156, 195.4988555908203, 297.8529357910156, 225.91700744628906], "page": 1, "span": [0, 170], "__ref_s3_data": null}], "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 119.2081069946289, 295.11798095703125, 157.60162353515625], "page": 1, "span": [0, 397], "__ref_s3_data": null}], "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 110.43414306640625, 197.8627471923828, 116.91976928710938], "page": 1, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.31700134277344, 101.67411041259766, 186.74652099609375, 108.18763732910156], "page": 1, "span": [0, 45], "__ref_s3_data": null}], "text": "\u00a9 2022 Copyright held by the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.55400085449219, 93.70310974121094, 157.03125, 100.21663665771484], "page": 1, "span": [0, 33], "__ref_s3_data": null}], "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 85.73310852050781, 166.94093322753906, 92.24663543701172], "page": 1, "span": [0, 39], "__ref_s3_data": null}], "text": "https://doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 232.48475646972656, 559.8057861328125, 251.91700744628906], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [317.9549865722656, 189.22499084472656, 379.82049560546875, 199.53408813476562], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "KEYWORDS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 164.9988250732422, 559.1859741210938, 184.3324432373047], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65997314453125, 144.41390991210938, 404.6536560058594, 151.94566345214844], "page": 1, "span": [0, 21], "__ref_s3_data": null}], "text": "ACM Reference Format:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 84.62297058105469, 559.5494995117188, 141.88003540039062], "page": 1, "span": [0, 374], "__ref_s3_data": null}], "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 2, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 695.8309936523438, 156.52899169921875, 706.14013671875], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "1 INTRODUCTION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 563.0528564453125, 303.0169677734375, 681.0164794921875], "page": 2, "span": [0, 702], "__ref_s3_data": null}], "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.52899932861328, 289.0808410644531, 295.5641174316406, 560.4684448242188], "page": 2, "span": [0, 1580], "__ref_s3_data": null}], "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.59199905395508, 212.36782836914062, 295.56396484375, 286.4964599609375], "page": 2, "span": [0, 462], "__ref_s3_data": null}], "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 177.12582397460938, 295.5616455078125, 207.41844177246094], "page": 2, "span": [0, 149], "__ref_s3_data": null}], "text": "- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 155.20883178710938, 294.2625427246094, 174.54144287109375], "page": 2, "span": [0, 109], "__ref_s3_data": null}], "text": "- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 122.33183288574219, 294.6838073730469, 152.62445068359375], "page": 2, "span": [0, 180], "__ref_s3_data": null}], "text": "- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 100.41383361816406, 295.56439208984375, 119.7474365234375], "page": 2, "span": [0, 115], "__ref_s3_data": null}], "text": "- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.672000885009766, 83.2601089477539, 216.02749633789062, 89.77363586425781], "page": 2, "span": [0, 60], "__ref_s3_data": null}], "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [342.0950012207031, 685.3028564453125, 558.4320068359375, 704.636474609375], "page": 2, "span": [0, 86], "__ref_s3_data": null}], "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [328.8650207519531, 630.5088500976562, 559.7210083007812, 682.718505859375], "page": 2, "span": [0, 280], "__ref_s3_data": null}], "text": "- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.62298583984375, 571.8138427734375, 559.1903076171875, 624.0244750976562], "page": 2, "span": [0, 297], "__ref_s3_data": null}], "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.7309875488281, 484.142822265625, 559.5819702148438, 569.2294311523438], "page": 2, "span": [0, 506], "__ref_s3_data": null}], "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 460.4820251464844, 421.7441101074219, 470.7911071777344], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "2 RELATED WORK", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.5249938964844, 327.7038269042969, 559.7161254882812, 445.6674499511719], "page": 2, "span": [0, 655], "__ref_s3_data": null}], "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 240.03182983398438, 559.1864624023438, 325.1194763183594], "page": 2, "span": [0, 500], "__ref_s3_data": null}], "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 216.37100219726562, 477.4568786621094, 226.6800994873047], "page": 2, "span": [0, 23], "__ref_s3_data": null}], "text": "3 THE DOCLAYNET DATASET", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 116.46983337402344, 559.7131958007812, 201.5564422607422], "page": 2, "span": [0, 522], "__ref_s3_data": null}], "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.59282684326172, 558.2041015625, 113.88543701171875], "page": 2, "span": [0, 186], "__ref_s3_data": null}], "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 3, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 3, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 536.4527587890625, 294.0437316894531, 555.885009765625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [53.79800033569336, 425.1098327636719, 294.2738342285156, 510.19647216796875], "page": 3, "span": [0, 513], "__ref_s3_data": null}], "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.57400131225586, 282.6438293457031, 295.5604553222656, 422.52545166015625], "page": 3, "span": [0, 810], "__ref_s3_data": null}], "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 184.01382446289062, 295.5615539550781, 280.0594482421875], "page": 3, "span": [0, 535], "__ref_s3_data": null}], "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 107.30182647705078, 295.56396484375, 181.429443359375], "page": 3, "span": [0, 413], "__ref_s3_data": null}], "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.83010864257812, 195.78997802734375, 90.34363555908203], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [317.62298583984375, 630.5088500976562, 559.1918334960938, 704.636474609375], "page": 3, "span": [0, 435], "__ref_s3_data": null}], "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 520.9197998046875, 558.4381103515625, 627.9244384765625], "page": 3, "span": [0, 645], "__ref_s3_data": null}], "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [316.9419860839844, 203.11082458496094, 559.7215576171875, 518.33544921875], "page": 3, "span": [0, 1854], "__ref_s3_data": null}], "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 174.8409881591797, 470.2132568359375, 185.15008544921875], "page": 3, "span": [0, 21], "__ref_s3_data": null}], "text": "4 ANNOTATION CAMPAIGN", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 85.8978271484375, 559.7138061523438, 160.0264434814453], "page": 3, "span": [0, 457], "__ref_s3_data": null}], "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 4, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 676.65380859375, 558.4896850585938, 707.0450439453125], "page": 4, "span": [0, 348], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.79800033569336, 185.68075561523438, 295.64874267578125, 237.99000549316406], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.46699905395508, 116.45683288574219, 294.0474548339844, 157.7084503173828], "page": 4, "span": [0, 231], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.57982635498047, 295.5584411621094, 113.989013671875], "page": 4, "span": [0, 193], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 416.7518310546875, 559.1853637695312, 479.92047119140625], "page": 4, "span": [0, 376], "__ref_s3_data": null}], "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 285.2448425292969, 559.7130737304688, 414.1674499511719], "page": 4, "span": [0, 746], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 98.9438247680664, 559.7176513671875, 282.7770080566406], "page": 4, "span": [0, 1159], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.13311004638672, 369.2456970214844, 89.64663696289062], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "$^{3}$https://arxiv.org/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 5, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 685.2938842773438, 294.04541015625, 704.636474609375], "page": 5, "span": [0, 135], "__ref_s3_data": null}], "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 542.8378295898438, 295.5592346191406, 682.7184448242188], "page": 5, "span": [0, 812], "__ref_s3_data": null}], "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 455.16583251953125, 295.56005859375, 540.2534790039062], "page": 5, "span": [0, 465], "__ref_s3_data": null}], "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 402.22686767578125, 294.04620361328125, 443.4874572753906], "page": 5, "span": [0, 202], "__ref_s3_data": null}], "text": "- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70799255371094, 358.39984130859375, 295.563720703125, 399.6514892578125], "page": 5, "span": [0, 208], "__ref_s3_data": null}], "text": "- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 336.4728698730469, 294.0472412109375, 355.81549072265625], "page": 5, "span": [0, 82], "__ref_s3_data": null}], "text": "- (3) For every Caption , there must be exactly one corresponding Picture or Table .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 314.5648193359375, 294.0459899902344, 333.8984680175781], "page": 5, "span": [0, 70], "__ref_s3_data": null}], "text": "- (4) Connected sub-pictures are grouped together in one Picture object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 303.59686279296875, 264.5057067871094, 311.98046875], "page": 5, "span": [0, 53], "__ref_s3_data": null}], "text": "- (5) Formula numbers are included in a Formula object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.7080078125, 270.72882080078125, 294.0461730957031, 301.021484375], "page": 5, "span": [0, 160], "__ref_s3_data": null}], "text": "- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.52899932861328, 217.798828125, 295.5625305175781, 259.0494689941406], "page": 5, "span": [0, 221], "__ref_s3_data": null}], "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 86.29182434082031, 295.562255859375, 215.3310089111328], "page": 5, "span": [0, 792], "__ref_s3_data": null}], "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 288.11480712890625, 559.8057861328125, 318.5060119628906], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [400.12841796875, 331.43994140625, 476.331787109375, 333.5567321777344], "page": 5, "span": [0, 64], "__ref_s3_data": null}], "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 247.1688232421875, 558.204345703125, 266.5024719238281], "page": 5, "span": [0, 123], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 82.78482818603516, 559.7149047851562, 244.7010040283203], "page": 5, "span": [0, 987], "__ref_s3_data": null}], "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 6, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 608.98291015625, 295.64874267578125, 705.1270751953125], "page": 6, "span": [0, 489], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.52899932861328, 215.43682861328125, 295.5561218261719, 421.07244873046875], "page": 6, "span": [0, 1252], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 193.5609893798828, 147.4853515625, 203.87008666992188], "page": 6, "span": [0, 13], "__ref_s3_data": null}], "text": "5 EXPERIMENTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.48400115966797, 82.7008285522461, 295.4281005859375, 178.74644470214844], "page": 6, "span": [0, 584], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 449.7158203125, 559.8057861328125, 512.9840087890625], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.9549865722656, 388.6548156738281, 558.2041625976562, 407.98846435546875], "page": 6, "span": [0, 102], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6409912109375, 311.9428405761719, 558.4364013671875, 386.0704650878906], "page": 6, "span": [0, 397], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 284.8690185546875, 466.8532409667969, 295.1781005859375], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.7489929199219, 85.2998275756836, 558.4308471679688, 279.9754638671875], "page": 6, "span": [0, 1146], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 7, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 7, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 663.77685546875, 295.6486511230469, 705.1270751953125], "page": 7, "span": [0, 205], "__ref_s3_data": null}], "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65899658203125, 663.7767944335938, 559.8068237304688, 705.1270141601562], "page": 7, "span": [0, 189], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [53.79800033569336, 462.1210021972656, 131.05624389648438, 472.4300842285156], "page": 7, "span": [0, 14], "__ref_s3_data": null}], "text": "Learning Curve", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [52.78499984741211, 262.55181884765625, 295.558349609375, 457.22845458984375], "page": 7, "span": [0, 1157], "__ref_s3_data": null}], "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 239.1809844970703, 164.3289794921875, 249.49008178710938], "page": 7, "span": [0, 22], "__ref_s3_data": null}], "text": "Impact of Class Labels", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 83.44783020019531, 295.5567932128906, 234.2884521484375], "page": 7, "span": [0, 910], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [317.6860046386719, 375.50982666015625, 559.5849609375, 460.5964660644531], "page": 7, "span": [0, 469], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549560546875, 352.2960205078125, 549.860595703125, 362.6051025390625], "page": 7, "span": [0, 46], "__ref_s3_data": null}], "text": "Impact of Document Split in Train and Test Set", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.62298583984375, 196.5628204345703, 559.7138061523438, 347.4034729003906], "page": 7, "span": [0, 852], "__ref_s3_data": null}], "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 173.34898376464844, 418.5477600097656, 183.6580810546875], "page": 7, "span": [0, 18], "__ref_s3_data": null}], "text": "Dataset Comparison", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 83.35986328125, 559.1881713867188, 168.45645141601562], "page": 7, "span": [0, 521], "__ref_s3_data": null}], "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 641.85888671875, 295.648681640625, 705.1270751953125], "page": 8, "span": [0, 298], "__ref_s3_data": null}], "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [53.79800033569336, 348.85986328125, 294.047119140625, 401.0794677734375], "page": 8, "span": [0, 295], "__ref_s3_data": null}], "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 206.40382385253906, 295.55908203125, 346.28546142578125], "page": 8, "span": [0, 793], "__ref_s3_data": null}], "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 176.62998962402344, 156.00534057617188, 186.9390869140625], "page": 8, "span": [0, 19], "__ref_s3_data": null}], "text": "Example Predictions", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 86.64982604980469, 295.5584411621094, 171.7364501953125], "page": 8, "span": [0, 481], "__ref_s3_data": null}], "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.95501708984375, 695.8309936523438, 405.7296142578125, 706.14013671875], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "6 CONCLUSION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 605.850830078125, 559.7137451171875, 690.9384765625], "page": 8, "span": [0, 507], "__ref_s3_data": null}], "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6860046386719, 507.2208251953125, 559.717041015625, 603.2664794921875], "page": 8, "span": [0, 573], "__ref_s3_data": null}], "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 474.3438415527344, 558.4346923828125, 504.636474609375], "page": 8, "span": [0, 188], "__ref_s3_data": null}], "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 446.5990295410156, 387.3695983886719, 456.9081115722656], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "REFERENCES", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [321.197998046875, 420.8371276855469, 558.2009887695312, 443.29766845703125], "page": 8, "span": [0, 191], "__ref_s3_data": null}], "text": "- [1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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IEEE Computer Society, dec 2015.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 141.8831329345703, 558.201416015625, 164.3436279296875], "page": 8, "span": [0, 227], "__ref_s3_data": null}], "text": "- [11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 117.98011016845703, 559.278076171875, 140.43362426757812], "page": 8, "span": [0, 192], "__ref_s3_data": null}], "text": "- [12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 86.09910583496094, 558.9715576171875, 116.52364349365234], "page": 8, "span": [0, 305], "__ref_s3_data": null}], "text": "- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 9, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 9, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [62.323875427246094, 343.73516845703125, 318.5047302246094, 349.7145690917969], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [53.79800033569336, 286.16876220703125, 559.807861328125, 327.51800537109375], "page": 9, "span": [0, 386], "__ref_s3_data": null}], "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [69.23400115966797, 242.4801025390625, 295.22406005859375, 264.93365478515625], "page": 9, "span": [0, 195], "__ref_s3_data": null}], "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 218.56314086914062, 295.12176513671875, 241.02362060546875], "page": 9, "span": [0, 190], "__ref_s3_data": null}], "text": "- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. 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Microsoft COCO: common objects in context, 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 162.77911376953125, 295.1200866699219, 177.26263427734375], "page": 9, "span": [0, 100], "__ref_s3_data": null}], "text": "- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 122.92810821533203, 294.80889892578125, 161.3226318359375], "page": 9, "span": [0, 339], "__ref_s3_data": null}], "text": "- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.797996520996094, 83.07810974121094, 295.22174072265625, 121.47162628173828], "page": 9, "span": [0, 336], "__ref_s3_data": null}], "text": "- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 250.45010375976562, 559.0263671875, 264.9336242675781], "page": 9, "span": [0, 153], "__ref_s3_data": null}], "text": "- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 226.54010009765625, 558.9714965820312, 248.99362182617188], "page": 9, "span": [0, 188], "__ref_s3_data": null}], "text": "- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 194.65213012695312, 559.275390625, 225.08364868164062], "page": 9, "span": [0, 290], "__ref_s3_data": null}], "text": "- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 178.71212768554688, 559.3782958984375, 193.20263671875], "page": 9, "span": [0, 138], "__ref_s3_data": null}], "text": "- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [323.4083557128906, 266.14923095703125, 553.295166015625, 541.6513671875], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [88.32994079589844, 571.43212890625, 263.7051696777344, 699.1134643554688], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.059146881103516, 251.1358642578125, 295.8506164550781, 481.2087097167969], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.9601745605469, 332.31915283203125, 559.396484375, 706.6611938476562], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.484130859375, 531.9901733398438, 553.5410766601562, 702.1138305664062], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [52.96445846557617, 349.8663635253906, 556.9306030273438, 707.2642822265625], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [98.93096923828125, 497.91845703125, 512.5798950195312, 654.5245361328125], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [336.6930236816406, 553.3746948242188, 439.457275390625, 599.942626953125], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79803466796875, 533.9879760742188, 111.94354248046875, 544.297119140625], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "ABSTRACT", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 257.7068176269531, 295.5601806640625, 529.095458984375], "page": 1, "span": [0, 1595], "__ref_s3_data": null}], "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 230.69398498535156, 134.81988525390625, 241.00308227539062], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "CCS CONCEPTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.79798889160156, 195.4988555908203, 297.8529357910156, 225.91700744628906], "page": 1, "span": [0, 170], "__ref_s3_data": null}], "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 119.2081069946289, 295.11798095703125, 157.60162353515625], "page": 1, "span": [0, 397], "__ref_s3_data": null}], "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 110.43414306640625, 197.8627471923828, 116.91976928710938], "page": 1, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.31700134277344, 101.67411041259766, 186.74652099609375, 108.18763732910156], "page": 1, "span": [0, 45], "__ref_s3_data": null}], "text": "\u00a9 2022 Copyright held by the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.55400085449219, 93.70310974121094, 157.03125, 100.21663665771484], "page": 1, "span": [0, 33], "__ref_s3_data": null}], "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 85.73310852050781, 166.94093322753906, 92.24663543701172], "page": 1, "span": [0, 39], "__ref_s3_data": null}], "text": "https://doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 232.48475646972656, 559.8057861328125, 251.91700744628906], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [317.9549865722656, 189.22499084472656, 379.82049560546875, 199.53408813476562], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "KEYWORDS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 164.9988250732422, 559.1859741210938, 184.3324432373047], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65997314453125, 144.41390991210938, 404.6536560058594, 151.94566345214844], "page": 1, "span": [0, 21], "__ref_s3_data": null}], "text": "ACM Reference Format:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 84.62297058105469, 559.5494995117188, 141.88003540039062], "page": 1, "span": [0, 374], "__ref_s3_data": null}], "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 2, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 695.8309936523438, 156.52899169921875, 706.14013671875], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "1 INTRODUCTION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 563.0528564453125, 303.0169677734375, 681.0164794921875], "page": 2, "span": [0, 702], "__ref_s3_data": null}], "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.52899932861328, 289.0808410644531, 295.5641174316406, 560.4684448242188], "page": 2, "span": [0, 1580], "__ref_s3_data": null}], "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.59199905395508, 212.36782836914062, 295.56396484375, 286.4964599609375], "page": 2, "span": [0, 462], "__ref_s3_data": null}], "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 177.12582397460938, 295.5616455078125, 207.41844177246094], "page": 2, "span": [0, 149], "__ref_s3_data": null}], "text": "- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 155.20883178710938, 294.2625427246094, 174.54144287109375], "page": 2, "span": [0, 109], "__ref_s3_data": null}], "text": "- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 122.33183288574219, 294.6838073730469, 152.62445068359375], "page": 2, "span": [0, 180], "__ref_s3_data": null}], "text": "- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 100.41383361816406, 295.56439208984375, 119.7474365234375], "page": 2, "span": [0, 115], "__ref_s3_data": null}], "text": "- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.672000885009766, 83.2601089477539, 216.02749633789062, 89.77363586425781], "page": 2, "span": [0, 60], "__ref_s3_data": null}], "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [342.0950012207031, 685.3028564453125, 558.4320068359375, 704.636474609375], "page": 2, "span": [0, 86], "__ref_s3_data": null}], "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [328.8650207519531, 630.5088500976562, 559.7210083007812, 682.718505859375], "page": 2, "span": [0, 280], "__ref_s3_data": null}], "text": "- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.62298583984375, 571.8138427734375, 559.1903076171875, 624.0244750976562], "page": 2, "span": [0, 297], "__ref_s3_data": null}], "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.7309875488281, 484.142822265625, 559.5819702148438, 569.2294311523438], "page": 2, "span": [0, 506], "__ref_s3_data": null}], "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 460.4820251464844, 421.7441101074219, 470.7911071777344], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "2 RELATED WORK", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.5249938964844, 327.7038269042969, 559.7161254882812, 445.6674499511719], "page": 2, "span": [0, 655], "__ref_s3_data": null}], "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 240.03182983398438, 559.1864624023438, 325.1194763183594], "page": 2, "span": [0, 500], "__ref_s3_data": null}], "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 216.37100219726562, 477.4568786621094, 226.6800994873047], "page": 2, "span": [0, 23], "__ref_s3_data": null}], "text": "3 THE DOCLAYNET DATASET", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 116.46983337402344, 559.7131958007812, 201.5564422607422], "page": 2, "span": [0, 522], "__ref_s3_data": null}], "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.59282684326172, 558.2041015625, 113.88543701171875], "page": 2, "span": [0, 186], "__ref_s3_data": null}], "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 3, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 3, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 536.4527587890625, 294.0437316894531, 555.885009765625], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [53.79800033569336, 425.1098327636719, 294.2738342285156, 510.19647216796875], "page": 3, "span": [0, 513], "__ref_s3_data": null}], "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.57400131225586, 282.6438293457031, 295.5604553222656, 422.52545166015625], "page": 3, "span": [0, 810], "__ref_s3_data": null}], "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 184.01382446289062, 295.5615539550781, 280.0594482421875], "page": 3, "span": [0, 535], "__ref_s3_data": null}], "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 107.30182647705078, 295.56396484375, 181.429443359375], "page": 3, "span": [0, 413], "__ref_s3_data": null}], "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.83010864257812, 195.78997802734375, 90.34363555908203], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [317.62298583984375, 630.5088500976562, 559.1918334960938, 704.636474609375], "page": 3, "span": [0, 435], "__ref_s3_data": null}], "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 520.9197998046875, 558.4381103515625, 627.9244384765625], "page": 3, "span": [0, 645], "__ref_s3_data": null}], "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [316.9419860839844, 203.11082458496094, 559.7215576171875, 518.33544921875], "page": 3, "span": [0, 1854], "__ref_s3_data": null}], "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 174.8409881591797, 470.2132568359375, 185.15008544921875], "page": 3, "span": [0, 21], "__ref_s3_data": null}], "text": "4 ANNOTATION CAMPAIGN", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 85.8978271484375, 559.7138061523438, 160.0264434814453], "page": 3, "span": [0, 457], "__ref_s3_data": null}], "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 4, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 676.65380859375, 558.4896850585938, 707.0450439453125], "page": 4, "span": [0, 348], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.79800033569336, 185.68075561523438, 295.64874267578125, 237.99000549316406], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.46699905395508, 116.45683288574219, 294.0474548339844, 157.7084503173828], "page": 4, "span": [0, 231], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 83.57982635498047, 295.5584411621094, 113.989013671875], "page": 4, "span": [0, 193], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 416.7518310546875, 559.1853637695312, 479.92047119140625], "page": 4, "span": [0, 376], "__ref_s3_data": null}], "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 285.2448425292969, 559.7130737304688, 414.1674499511719], "page": 4, "span": [0, 746], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 98.9438247680664, 559.7176513671875, 282.7770080566406], "page": 4, "span": [0, 1159], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 83.13311004638672, 369.2456970214844, 89.64663696289062], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "$^{3}$https://arxiv.org/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 5, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.79800033569336, 685.2938842773438, 294.04541015625, 704.636474609375], "page": 5, "span": [0, 135], "__ref_s3_data": null}], "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 542.8378295898438, 295.5592346191406, 682.7184448242188], "page": 5, "span": [0, 812], "__ref_s3_data": null}], "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 455.16583251953125, 295.56005859375, 540.2534790039062], "page": 5, "span": [0, 465], "__ref_s3_data": null}], "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.70800018310547, 402.22686767578125, 294.04620361328125, 443.4874572753906], "page": 5, "span": [0, 202], "__ref_s3_data": null}], "text": "- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70799255371094, 358.39984130859375, 295.563720703125, 399.6514892578125], "page": 5, "span": [0, 208], "__ref_s3_data": null}], "text": "- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 336.4728698730469, 294.0472412109375, 355.81549072265625], "page": 5, "span": [0, 82], "__ref_s3_data": null}], "text": "- (3) For every Caption , there must be exactly one corresponding Picture or Table .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 314.5648193359375, 294.0459899902344, 333.8984680175781], "page": 5, "span": [0, 70], "__ref_s3_data": null}], "text": "- (4) Connected sub-pictures are grouped together in one Picture object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.70800018310547, 303.59686279296875, 264.5057067871094, 311.98046875], "page": 5, "span": [0, 53], "__ref_s3_data": null}], "text": "- (5) Formula numbers are included in a Formula object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.7080078125, 270.72882080078125, 294.0461730957031, 301.021484375], "page": 5, "span": [0, 160], "__ref_s3_data": null}], "text": "- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.52899932861328, 217.798828125, 295.5625305175781, 259.0494689941406], "page": 5, "span": [0, 221], "__ref_s3_data": null}], "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 86.29182434082031, 295.562255859375, 215.3310089111328], "page": 5, "span": [0, 792], "__ref_s3_data": null}], "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 288.11480712890625, 559.8057861328125, 318.5060119628906], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [400.12841796875, 331.43994140625, 476.331787109375, 333.5567321777344], "page": 5, "span": [0, 64], "__ref_s3_data": null}], "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 247.1688232421875, 558.204345703125, 266.5024719238281], "page": 5, "span": [0, 123], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 82.78482818603516, 559.7149047851562, 244.7010040283203], "page": 5, "span": [0, 987], "__ref_s3_data": null}], "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 6, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 608.98291015625, 295.64874267578125, 705.1270751953125], "page": 6, "span": [0, 489], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.52899932861328, 215.43682861328125, 295.5561218261719, 421.07244873046875], "page": 6, "span": [0, 1252], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 193.5609893798828, 147.4853515625, 203.87008666992188], "page": 6, "span": [0, 13], "__ref_s3_data": null}], "text": "5 EXPERIMENTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.48400115966797, 82.7008285522461, 295.4281005859375, 178.74644470214844], "page": 6, "span": [0, 584], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 449.7158203125, 559.8057861328125, 512.9840087890625], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.9549865722656, 388.6548156738281, 558.2041625976562, 407.98846435546875], "page": 6, "span": [0, 102], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6409912109375, 311.9428405761719, 558.4364013671875, 386.0704650878906], "page": 6, "span": [0, 397], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 284.8690185546875, 466.8532409667969, 295.1781005859375], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.7489929199219, 85.2998275756836, 558.4308471679688, 279.9754638671875], "page": 6, "span": [0, 1146], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 7, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 7, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 663.77685546875, 295.6486511230469, 705.1270751953125], "page": 7, "span": [0, 205], "__ref_s3_data": null}], "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.65899658203125, 663.7767944335938, 559.8068237304688, 705.1270141601562], "page": 7, "span": [0, 189], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [53.79800033569336, 462.1210021972656, 131.05624389648438, 472.4300842285156], "page": 7, "span": [0, 14], "__ref_s3_data": null}], "text": "Learning Curve", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [52.78499984741211, 262.55181884765625, 295.558349609375, 457.22845458984375], "page": 7, "span": [0, 1157], "__ref_s3_data": null}], "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 239.1809844970703, 164.3289794921875, 249.49008178710938], "page": 7, "span": [0, 22], "__ref_s3_data": null}], "text": "Impact of Class Labels", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.46699905395508, 83.44783020019531, 295.5567932128906, 234.2884521484375], "page": 7, "span": [0, 910], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [317.6860046386719, 375.50982666015625, 559.5849609375, 460.5964660644531], "page": 7, "span": [0, 469], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549560546875, 352.2960205078125, 549.860595703125, 362.6051025390625], "page": 7, "span": [0, 46], "__ref_s3_data": null}], "text": "Impact of Document Split in Train and Test Set", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.62298583984375, 196.5628204345703, 559.7138061523438, 347.4034729003906], "page": 7, "span": [0, 852], "__ref_s3_data": null}], "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 173.34898376464844, 418.5477600097656, 183.6580810546875], "page": 7, "span": [0, 18], "__ref_s3_data": null}], "text": "Dataset Comparison", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.6860046386719, 83.35986328125, 559.1881713867188, 168.45645141601562], "page": 7, "span": [0, 521], "__ref_s3_data": null}], "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 558.202880859375, 731.6909790039062], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [53.50199890136719, 641.85888671875, 295.648681640625, 705.1270751953125], "page": 8, "span": [0, 298], "__ref_s3_data": null}], "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [53.79800033569336, 348.85986328125, 294.047119140625, 401.0794677734375], "page": 8, "span": [0, 295], "__ref_s3_data": null}], "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.46699905395508, 206.40382385253906, 295.55908203125, 346.28546142578125], "page": 8, "span": [0, 793], "__ref_s3_data": null}], "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 176.62998962402344, 156.00534057617188, 186.9390869140625], "page": 8, "span": [0, 19], "__ref_s3_data": null}], "text": "Example Predictions", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.52899932861328, 86.64982604980469, 295.5584411621094, 171.7364501953125], "page": 8, "span": [0, 481], "__ref_s3_data": null}], "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.95501708984375, 695.8309936523438, 405.7296142578125, 706.14013671875], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "6 CONCLUSION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.9549865722656, 605.850830078125, 559.7137451171875, 690.9384765625], "page": 8, "span": [0, 507], "__ref_s3_data": null}], "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.6860046386719, 507.2208251953125, 559.717041015625, 603.2664794921875], "page": 8, "span": [0, 573], "__ref_s3_data": null}], "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.62298583984375, 474.3438415527344, 558.4346923828125, 504.636474609375], "page": 8, "span": [0, 188], "__ref_s3_data": null}], "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.9549865722656, 446.5990295410156, 387.3695983886719, 456.9081115722656], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "REFERENCES", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [321.197998046875, 420.8371276855469, 558.2009887695312, 443.29766845703125], "page": 8, "span": [0, 191], "__ref_s3_data": null}], "text": "- [1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 388.9571228027344, 559.3798217773438, 419.38763427734375], "page": 8, "span": [0, 279], "__ref_s3_data": null}], "text": "- [2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. 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In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 301.2920837402344, 559.0262451171875, 331.7166442871094], "page": 8, "span": [0, 261], "__ref_s3_data": null}], "text": "- [5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 277.3751220703125, 558.20361328125, 299.83563232421875], "page": 8, "span": [0, 235], "__ref_s3_data": null}], "text": "- [6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.1979675292969, 237.53111267089844, 558.9714965820312, 275.9256286621094], "page": 8, "span": [0, 316], "__ref_s3_data": null}], "text": "- [7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 213.6141357421875, 558.9022216796875, 236.07464599609375], "page": 8, "span": [0, 172], "__ref_s3_data": null}], "text": "- [8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [321.197998046875, 181.74110412597656, 559.2744750976562, 212.16464233398438], "page": 8, "span": [0, 271], "__ref_s3_data": null}], "text": "- [9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 165.7931365966797, 558.2020263671875, 180.28463745117188], "page": 8, "span": [0, 149], "__ref_s3_data": null}], "text": "- [10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 141.8831329345703, 558.201416015625, 164.3436279296875], "page": 8, "span": [0, 227], "__ref_s3_data": null}], "text": "- [11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 117.98011016845703, 559.278076171875, 140.43362426757812], "page": 8, "span": [0, 192], "__ref_s3_data": null}], "text": "- [12] Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 86.09910583496094, 558.9715576171875, 116.52364349365234], "page": 8, "span": [0, 305], "__ref_s3_data": null}], "text": "- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 723.4239501953125, 347.0172424316406, 731.6909790039062], "page": 9, "span": [0, 71], "__ref_s3_data": null}], "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [365.75701904296875, 723.4239501953125, 558.2028198242188, 731.6909790039062], "page": 9, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [62.323875427246094, 343.73516845703125, 318.5047302246094, 349.7145690917969], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [53.79800033569336, 286.16876220703125, 559.807861328125, 327.51800537109375], "page": 9, "span": [0, 386], "__ref_s3_data": null}], "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [69.23400115966797, 242.4801025390625, 295.22406005859375, 264.93365478515625], "page": 9, "span": [0, 195], "__ref_s3_data": null}], "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.79800033569336, 218.56314086914062, 295.12176513671875, 241.02362060546875], "page": 9, "span": [0, 190], "__ref_s3_data": null}], "text": "- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 202.62213134765625, 294.042236328125, 217.1136474609375], "page": 9, "span": [0, 132], "__ref_s3_data": null}], "text": "- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798004150390625, 178.71910095214844, 295.2226257324219, 201.17263793945312], "page": 9, "span": [0, 219], "__ref_s3_data": null}], "text": "- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 162.77911376953125, 295.1200866699219, 177.26263427734375], "page": 9, "span": [0, 100], "__ref_s3_data": null}], "text": "- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.79800033569336, 122.92810821533203, 294.80889892578125, 161.3226318359375], "page": 9, "span": [0, 339], "__ref_s3_data": null}], "text": "- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.797996520996094, 83.07810974121094, 295.22174072265625, 121.47162628173828], "page": 9, "span": [0, 336], "__ref_s3_data": null}], "text": "- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 250.45010375976562, 559.0263671875, 264.9336242675781], "page": 9, "span": [0, 153], "__ref_s3_data": null}], "text": "- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 226.54010009765625, 558.9714965820312, 248.99362182617188], "page": 9, "span": [0, 188], "__ref_s3_data": null}], "text": "- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 194.65213012695312, 559.275390625, 225.08364868164062], "page": 9, "span": [0, 290], "__ref_s3_data": null}], "text": "- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.9549865722656, 178.71212768554688, 559.3782958984375, 193.20263671875], "page": 9, "span": [0, 138], "__ref_s3_data": null}], "text": "- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [323.408203125, 266.1492919921875, 553.2952270507812, 541.6512451171875], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [88.33030700683594, 571.4317626953125, 263.7049560546875, 699.1134643554688], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.05912780761719, 251.135986328125, 295.8506164550781, 481.2087097167969], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.960205078125, 332.31915283203125, 559.396484375, 706.6611938476562], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.48431396484375, 531.9892578125, 553.5411376953125, 702.1139526367188], "page": 6, "span": [0, 329], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [52.963985443115234, 349.8648986816406, 556.931640625, 707.26416015625], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [98.93103790283203, 497.9184875488281, 512.579833984375, 654.5245361328125], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. 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diff --git a/tests/data/groundtruth/docling_v1/2206.01062.pages.json b/tests/data/groundtruth/docling_v1/2206.01062.pages.json
index 95ae5fbc..f9b8b7bd 100644
--- a/tests/data/groundtruth/docling_v1/2206.01062.pages.json
+++ b/tests/data/groundtruth/docling_v1/2206.01062.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Ahmed S. Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}, {"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}, {"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}, {"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}, {"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}, {"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; 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327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107323408126831, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095849633216858, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.4083557128906, "t": 250.34860229492188, "r": 553.295166015625, "b": 525.8507690429688, "coord_origin": "TOPLEFT"}, "confidence": 0.5910328030586243, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107323408126831, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095849633216858, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.4083557128906, "t": 250.34860229492188, "r": 553.295166015625, "b": 525.8507690429688, "coord_origin": "TOPLEFT"}, "confidence": 0.5910328030586243, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208481311798096, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, 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{"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455161690711975, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8537878394126892, "cells": [{"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104616641998291, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32994079589844, "t": 92.88652801513672, "r": 263.7051696777344, "b": 220.56790161132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9698247909545898, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 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189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32994079589844, "t": 92.88652801513672, "r": 263.7051696777344, "b": 220.56790161132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9698247909545898, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, 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"coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 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281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104616641998291, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": 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{"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, 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53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 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"Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 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"children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.866277813911438, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 62.02744674682617, "t": 195.68003845214844, "r": 285.78948974609375, "b": 351.6619567871094, "coord_origin": "TOPLEFT"}, "confidence": 0.9883638620376587, "cells": [{"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, 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Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.866277813911438, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316120743751526, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318176507949829, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.829693615436554, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.3552017211914, "t": 150.93629455566406, "r": 267.0082702636719, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, 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"coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.9774475097656, "t": 150.79129028320312, "r": 522.9158935546875, "b": 306.2658996582031, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.829693615436554, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 80.3552017211914, "t": 150.93629455566406, "r": 267.0082702636719, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316120743751526, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318176507949829, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.65901947021484, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85406494140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, 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"bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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ACM, 2018.", "bbox": {"l": 409.56598, "t": 590.82736, "r": 507.31765999999993, "b": 597.3409, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018."}, {"label": "list_item", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 317.95499, "t": 598.79736, "r": 559.3783, "b": 613.28787, "coord_origin": "TOPLEFT"}, "confidence": 0.8972867131233215, "cells": [{"id": 78, "text": "[23]", "bbox": {"l": 317.95499, "t": 598.79736, "r": 329.37976, "b": 605.3109, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Connor Shorten and Taghi M. Khoshgoftaar. 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Journal of Big Data , 6(1):60, 2019."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021661043167114, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429464101791382, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
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Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}, {"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}, {"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}, {"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}, {"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}, {"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}, {"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "section_header", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}, "confidence": 0.8416301012039185, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 90.96701, "t": 133.67236000000003, "r": 193.73123, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8637146949768066, "cells": [{"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 255.11602999999997, "t": 133.67236000000003, "r": 357.88025, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8612666726112366, "cells": [{"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 419.26505, "t": 133.67236000000003, "r": 522.0293, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8760607838630676, "cells": [{"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.54303, "t": 192.05737, "r": 275.30725, "b": 238.62531, "coord_origin": "TOPLEFT"}, "confidence": 0.8319916129112244, "cells": [{"id": 14, "text": "Ahmed S. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149599671363831, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458832502365112, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107343673706055, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 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327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149599671363831, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458832502365112, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107343673706055, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095879435539246, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910214781761169, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149599671363831, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458832502365112, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107343673706055, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095879435539246, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910214781761169, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208477139472961, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, 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{"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455164670944214, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.853788435459137, "cells": [{"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682235836982727, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594228267669678, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584772348403931, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997144460678101, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563500285148621, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813902974128723, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844218492507935, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860379099845886, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852980375289917, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682235836982727, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594228267669678, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584772348403931, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997144460678101, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563500285148621, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813902974128723, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844218492507935, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860379099845886, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852980375289917, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594228267669678, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584772348403931, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997144460678101, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563500285148621, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813902974128723, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844218492507935, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860379099845886, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852980375289917, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682235836982727, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.891997754573822, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104520082473755, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88655090332031, "r": 263.7049560546875, "b": 220.5682373046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048354029655457, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422591328620911, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.891997754573822, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 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189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048354029655457, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422591328620911, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88655090332031, "r": 263.7049560546875, "b": 220.5682373046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, 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"coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 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281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048354029655457, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422591328620911, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.891997754573822, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104520082473755, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906610012054443, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.4754638671875, "r": 512.579833984375, "b": 294.0815124511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9860089421272278, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": 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{"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748397469520569, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847555756568909, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080349802970886, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847870469093323, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.4754638671875, "r": 512.579833984375, "b": 294.0815124511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9860089421272278, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906610012054443, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.4754638671875, "r": 512.579833984375, "b": 294.0815124511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9860089421272278, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.9803937673568726, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748397469520569, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847555756568909, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080349802970886, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847870469093323, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906610012054443, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.4754638671875, "r": 512.579833984375, "b": 294.0815124511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9860089421272278, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, 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53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.9803937673568726, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748397469520569, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847555756568909, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080349802970886, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847870469093323, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 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"Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 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"children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.902275800704956, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691525101661682, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544305205345154, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264117002487183, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873710632324219, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611717462539673, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.982954204082489, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.887782871723175, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534672856330872, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662874698638916, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 62.02753829956055, "t": 195.6800537109375, "r": 285.78955078125, "b": 351.6618957519531, "coord_origin": "TOPLEFT"}, "confidence": 0.9883638620376587, "cells": [{"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 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Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782690405845642, "cells": [{"id": 131, "text": "0", "bbox": {"l": 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317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782690405845642, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9639934301376343, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782690405845642, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9639934301376343, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579004049301147, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662874698638916, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, 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To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296932578086853, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572024941444397, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.87362140417099, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589216113090515, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316117763519287, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574697613716125, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859885573387146, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319982528686523, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854928851127625, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676100015640259, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372856616973877, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574697613716125, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859885573387146, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319982528686523, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854928851127625, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676100015640259, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372856616973877, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.6590347290039, "t": 172.48080444335938, "r": 274.83465576171875, "b": 339.8540344238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9892616271972656, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, 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"bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574697613716125, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859885573387146, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319982528686523, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854928851127625, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676100015640259, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372856616973877, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Journal of Big Data , 6(1):60, 2019."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021692037582397, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429245352745056, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
index 0ff591af..d5036268 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.json
@@ -1 +1 @@
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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. 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Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 273.8258056640625, 264.4082946777344, 286.3288879394531], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 173.6999969482422, 480.72003173828125, 269.9199523925781], "page": 1, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 612.7918090820312, 318.4514465332031, 625.2948608398438], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 536.5759887695312, 480.5956726074219, 608.8849487304688], "page": 1, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 464.017822265625, 480.5989074707031, 519.2052612304688], "page": 1, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 273.8258056640625, 264.4082946777344, 286.3288879394531], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 173.6999969482422, 480.72003173828125, 269.9199523925781], "page": 1, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 125.87999725341797, 480.59857177734375, 174.2779541015625], "page": 1, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [{"prov": [{"bbox": [139.6674041748047, 322.5054626464844, 475.00927734375, 454.4546203613281], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
index b6fb5969..e243d300 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 31, 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389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "0.955", "bbox": {"l": 363.67599, "t": 378.71048, "r": 384.66632, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "0.857", "bbox": {"l": 396.20599, "t": 378.71048, "r": 417.19632, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "5.39", "bbox": {"l": 440.767, "t": 378.71048, "r": 457.15039, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4", "bbox": {"l": 161.90601, "t": 397.53949, "r": 166.51474, "b": 408.82819, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4", "bbox": {"l": 209.509, "t": 397.53949, "r": 214.11774, "b": 408.82819, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 392.06049, "r": 271.41064, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "0.938", "bbox": {"l": 289.017, "t": 392.06049, "r": 310.00732, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.904", "bbox": {"l": 326.71701, "t": 392.06049, "r": 347.70734, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "0.927", "bbox": {"l": 363.67599, "t": 392.06049, "r": 384.66632, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0.853", "bbox": {"l": 394.61801, "t": 391.99771, "r": 418.77798, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1.97", "bbox": {"l": 439.52701, "t": 391.99771, "r": 458.38336, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 405.01147, "r": 272.94495, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "0.952", "bbox": {"l": 289.017, "t": 405.01147, "r": 310.00732, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "0.909", "bbox": {"l": 326.71701, "t": 405.01147, "r": 347.70734, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "0.938", "bbox": {"l": 362.08801, "t": 404.9486999999999, "r": 386.24799, "b": 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"bbox": {"l": 363.67599, "t": 418.3614799999999, "r": 384.66632, "b": 429.65018, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 418.29871, "r": 418.77798, "b": 429.70398, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 418.29871, "r": 458.38336, "b": 429.70398, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 431.31246999999996, "r": 272.94495, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 431.31246999999996, "r": 310.00732, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 431.31246999999996, "r": 347.70734, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 431.24969, "r": 386.24799, "b": 442.65497, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 431.31246999999996, "r": 417.19632, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 431.31246999999996, "r": 457.15039, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 450.14248999999995, "r": 166.51474, "b": 461.43118, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 444.66248, "r": 271.41064, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 444.66248, "r": 345.40439, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.957740306854248, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591906070709229, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373531937599182, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.json b/tests/data/groundtruth/docling_v1/2305.03393v1.json
index cd2a0398..7a0266da 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.json
@@ -1 +1 @@
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Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.11109924316406, 294.2145080566406, 452.2415771484375, 313.3060607910156], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 259.3119201660156, 228.933837890625, 269.88031005859375], "page": 1, "span": [0, 14], "__ref_s3_data": null}], "text": "1 Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76512145996094, 163.18548583984375, 480.595947265625, 243.7134552001953], "page": 1, "span": [0, 500], "__ref_s3_data": null}], "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 127.14546966552734, 480.5958251953125, 159.85244750976562], "page": 1, "span": [0, 235], "__ref_s3_data": null}], "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 2, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 591.7794189453125, 480.5918884277344, 665.6658325195312], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [134.76499938964844, 271.1133117675781, 480.5923156738281, 339.68621826171875], "page": 2, "span": [0, 435], "__ref_s3_data": null}], "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14530181884766, 480.5948181152344, 267.44927978515625], "page": 2, "span": [0, 911], "__ref_s3_data": null}], "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 3, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 580.5831298828125, 480.5918273925781, 673.0662231445312], "page": 3, "span": [0, 584], "__ref_s3_data": null}], "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.7701416015625, 480.5957336425781, 577.1641235351562], "page": 3, "span": [0, 721], "__ref_s3_data": null}], "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 352.9132385253906, 480.5956726074219, 457.35211181640625], "page": 3, "span": [0, 626], "__ref_s3_data": null}], "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 319.3436584472656, 236.76913452148438, 329.91204833984375], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "2 Related Work", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14423370361328, 484.1204833984375, 303.3141784667969], "page": 3, "span": [0, 1161], "__ref_s3_data": null}], "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 4, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 532.7620849609375, 480.59576416015625, 673.0662231445312], "page": 4, "span": [0, 939], "__ref_s3_data": null}], "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 305.3533020019531, 480.595703125, 529.3430786132812], "page": 4, "span": [0, 1404], "__ref_s3_data": null}], "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 209.4513397216797, 480.5937805175781, 301.93426513671875], "page": 4, "span": [0, 572], "__ref_s3_data": null}], "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 175.88177490234375, 269.6244201660156, 186.45016479492188], "page": 4, "span": [0, 19], "__ref_s3_data": null}], "text": "3 Problem Statement", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14434051513672, 480.59368896484375, 159.85231018066406], "page": 4, "span": [0, 233], "__ref_s3_data": null}], "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 5, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 604.4931640625, 480.5937805175781, 673.0662231445312], "page": 5, "span": [0, 422], "__ref_s3_data": null}], "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.6070098876953, 562.7882080078125, 469.7522277832031, 570.9207153320312], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [134.76499938964844, 259.57940673828125, 480.5947570800781, 423.793212890625], "page": 5, "span": [0, 1021], "__ref_s3_data": null}], "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 211.29440307617188, 480.5928955078125, 255.95736694335938], "page": 5, "span": [0, 313], "__ref_s3_data": null}], "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14539337158203, 480.5947265625, 207.67337036132812], "page": 5, "span": [0, 542], "__ref_s3_data": null}], "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 6, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.59478759765625, 673.0662231445312], "page": 6, "span": [0, 132], "__ref_s3_data": null}], "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 496.2580871582031, 480.595703125, 648.5172119140625], "page": 6, "span": [0, 977], "__ref_s3_data": null}], "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.8005065917969, 372.50848388671875, 471.368896484375], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "4 Optimised Table Structure Language", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 350.400146484375, 480.5947265625, 442.8830261230469], "page": 6, "span": [0, 563], "__ref_s3_data": null}], "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 317.3211364746094, 261.80108642578125, 326.1280822753906], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "4.1 Language Definition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 270.2941589355469, 480.5887145996094, 303.0021057128906], "page": 6, "span": [0, 165], "__ref_s3_data": null}], "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.708984375, 257.701171875, 409.3113708496094, 266.4981384277344], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.99298095703125, 235.22317504882812, 460.54443359375, 244.0301055908203], "page": 6, "span": [0, 72], "__ref_s3_data": null}], "text": "- -\"C\" cell a new table cell that either has or does not have cell content", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.99301147460938, 210.6751708984375, 480.59393310546875, 231.43710327148438], "page": 6, "span": [0, 82], "__ref_s3_data": null}], "text": "- -\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 186.1261749267578, 480.58856201171875, 206.8881072998047], "page": 6, "span": [0, 81], "__ref_s3_data": null}], "text": "- -\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 173.53317260742188, 454.5549621582031, 182.34010314941406], "page": 6, "span": [0, 71], "__ref_s3_data": null}], "text": "- -\"X\" cell cross cell , to merge with both left and upper neighbor cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 160.93917846679688, 328.61676025390625, 169.74610900878906], "page": 6, "span": [0, 40], "__ref_s3_data": null}], "text": "- -\"NL\" new-line , switch to the next row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76504516601562, 127.14515686035156, 480.5928039550781, 147.8971405029297], "page": 6, "span": [0, 99], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 7, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 636.1503295898438, 480.58740234375, 666.2008056640625], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.76499938964844, 477.8972473144531, 246.6519775390625, 486.7041931152344], "page": 7, "span": [0, 19], "__ref_s3_data": null}], "text": "4.2 Language Syntax", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 457.95526123046875, 363.7961730957031, 466.7522277832031], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.97299194335938, 424.0662536621094, 480.5890197753906, 444.8291931152344], "page": 7, "span": [0, 108], "__ref_s3_data": null}], "text": "- 1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 400.15325927734375, 480.59228515625, 420.9151916503906], "page": 7, "span": [0, 106], "__ref_s3_data": null}], "text": "- 2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 388.19525146484375, 226.0736083984375, 397.002197265625], "page": 7, "span": [0, 20], "__ref_s3_data": null}], "text": "3. Cross cell rule :", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [151.70098876953125, 352.3262939453125, 480.5923767089844, 385.0332336425781], "page": 7, "span": [0, 167], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 340.3673095703125, 474.5901794433594, 349.17425537109375], "page": 7, "span": [0, 78], "__ref_s3_data": null}], "text": "- 4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 316.4543151855469, 480.58746337890625, 337.21624755859375], "page": 7, "span": [0, 84], "__ref_s3_data": null}], "text": "- 5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 292.5403137207031, 480.5945739746094, 313.3032531738281], "page": 7, "span": [0, 144], "__ref_s3_data": null}], "text": "- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76498413085938, 151.05833435058594, 480.5958251953125, 279.40728759765625], "page": 7, "span": [0, 848], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14533233642578, 480.5926513671875, 147.89730834960938], "page": 7, "span": [0, 153], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 8, "span": [0, 1], "__ref_s3_data": null}], "text": "8", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 8, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.5888366699219, 673.0662231445312], "page": 8, "span": [0, 84], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 621.63623046875, 319.3470764160156, 630.4431762695312], "page": 8, "span": [0, 35], "__ref_s3_data": null}], "text": "4.3 Error-detection and -mitigation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 493.32415771484375, 480.59576416015625, 609.7182006835938], "page": 8, "span": [0, 797], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 460.2676086425781, 229.03533935546875, 470.83599853515625], "page": 8, "span": [0, 13], "__ref_s3_data": null}], "text": "5 Experiments", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 340.3122863769531, 480.59527587890625, 444.7501525878906], "page": 8, "span": [0, 684], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 288.2603454589844, 480.5908203125, 307.35186767578125], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59173583984375, 171.80722045898438], "page": 8, "span": [0, 299], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 9, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 9, "span": [0, 1], "__ref_s3_data": null}], "text": "9", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 640.3582153320312, 480.5957946777344, 673.0662231445312], "page": 9, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.0072021484375, 318.44842529296875, 622.8141479492188], "page": 9, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 537.8411254882812, 480.5927734375, 606.4141845703125], "page": 9, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 464.9591979980469, 480.59539794921875, 516.9276733398438], "page": 9, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 275.041259765625, 264.4033203125, 283.84820556640625], "page": 9, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 174.9652557373047, 480.59576416015625, 267.44921875], "page": 9, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59576416015625, 171.80722045898438], "page": 9, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 10, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 646.1133422851562, 480.59356689453125, 676.163818359375], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.76499938964844, 494.27825927734375, 257.0867919921875, 503.085205078125], "page": 10, "span": [0, 23], "__ref_s3_data": null}], "text": "5.3 Qualitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 425.5223083496094, 480.5898132324219, 482.13922119140625], "page": 10, "span": [0, 309], "__ref_s3_data": null}], "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 352.2828369140625, 480.591064453125, 394.4098815917969], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [227.91465759277344, 116.65360260009766, 230.10028076171875, 126.1739730834961], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u03bc", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [300.58056640625, 98.57134246826172, 302.72637939453125, 108.3780517578125], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u2265", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 11, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.2323608398438, 480.58837890625, 666.2008056640625], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 12, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 663.8826293945312, 219.25479125976562, 674.4510498046875], "page": 12, "span": [0, 12], "__ref_s3_data": null}], "text": "6 Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 588.5181884765625, 480.595703125, 645.13623046875], "page": 12, "span": [0, 330], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 468.1632080078125, 480.59478759765625, 584.5562133789062], "page": 12, "span": [0, 724], "__ref_s3_data": null}], "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 323.8973388671875, 480.5948181152344, 464.201171875], "page": 12, "span": [0, 926], "__ref_s3_data": null}], "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 287.61077880859375, 197.68641662597656, 298.1791687011719], "page": 12, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 228.12855529785156, 480.5920104980469, 269.1201477050781], "page": 12, "span": [0, 270], "__ref_s3_data": null}], "text": "- 1. 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ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 379.2555236816406, 480.59295654296875, 420.2022705078125], "page": 13, "span": [0, 295], "__ref_s3_data": null}], "text": "- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 335.4085388183594, 480.5946960449219, 376.35528564453125], "page": 13, "span": [0, 281], "__ref_s3_data": null}], "text": "- 12. 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In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 247.7145538330078, 480.5928649902344, 288.66131591796875], "page": 13, "span": [0, 241], "__ref_s3_data": null}], "text": "- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 181.90472412109375, 480.5958251953125, 244.81431579589844], "page": 13, "span": [0, 405], "__ref_s3_data": null}], "text": "- 15. 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Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 14, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 642.3383178710938, 480.59112548828125, 672.3259887695312], "page": 14, "span": [0, 223], "__ref_s3_data": null}], "text": "- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 598.45751953125, 480.5946960449219, 639.4490356445312], "page": 14, "span": [0, 269], "__ref_s3_data": null}], "text": "- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.7649688720703, 576.5853881835938, 480.5935363769531, 595.6130981445312], "page": 14, "span": [0, 147], "__ref_s3_data": null}], "text": "- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 521.74560546875, 480.5930480957031, 573.6961059570312], "page": 14, "span": [0, 329], "__ref_s3_data": null}], "text": "- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 477.9544982910156, 480.5955810546875, 518.9011840820312], "page": 14, "span": [0, 259], "__ref_s3_data": null}], "text": "- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 445.0785217285156, 480.59454345703125, 475.0652770996094], "page": 14, "span": [0, 206], "__ref_s3_data": null}], "text": "- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [148.45364379882812, 366.1532287597656, 464.3611145019531, 583.6258544921875], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [137.41453552246094, 451.76953125, 476.5608215332031, 558.48779296875], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [164.65020751953125, 511.6589050292969, 449.5506591796875, 628.202880859375], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [140.7096405029297, 198.32275390625, 472.7337951660156, 283.93609619140625], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [162.6731414794922, 128.786376953125, 451.7003479003906, 347.3775634765625], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [168.39266967773438, 157.9967041015625, 447.3527526855469, 610.0320434570312], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [139.66845703125, 322.5279235839844, 475.00372314453125, 454.4252624511719], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 7, "data": [[{"bbox": [160.3699951171875, 442.1952819824219, 168.0479278564453, 450.2650451660156], "spans": [[0, 0]], "text": "#", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [207.9739990234375, 442.1952819824219, 215.6519317626953, 450.2650451660156], "spans": [[0, 1]], "text": "#", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [239.79800415039062, 436.7162780761719, 278.3176574707031, 444.7860412597656], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, 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Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.11109924316406, 294.2145080566406, 452.2415771484375, 313.3060607910156], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 259.3119201660156, 228.933837890625, 269.88031005859375], "page": 1, "span": [0, 14], "__ref_s3_data": null}], "text": "1 Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76512145996094, 163.18548583984375, 480.595947265625, 243.7134552001953], "page": 1, "span": [0, 500], "__ref_s3_data": null}], "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76512145996094, 127.14546966552734, 480.5958251953125, 159.85244750976562], "page": 1, "span": [0, 235], "__ref_s3_data": null}], "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 2, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 2, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 591.7794189453125, 480.5918884277344, 665.6658325195312], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [134.76499938964844, 271.1133117675781, 480.5923156738281, 339.68621826171875], "page": 2, "span": [0, 435], "__ref_s3_data": null}], "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14530181884766, 480.5948181152344, 267.44927978515625], "page": 2, "span": [0, 911], "__ref_s3_data": null}], "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 3, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 3, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 580.5831298828125, 480.5918273925781, 673.0662231445312], "page": 3, "span": [0, 584], "__ref_s3_data": null}], "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.7701416015625, 480.5957336425781, 577.1641235351562], "page": 3, "span": [0, 721], "__ref_s3_data": null}], "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 352.9132385253906, 480.5956726074219, 457.35211181640625], "page": 3, "span": [0, 626], "__ref_s3_data": null}], "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 319.3436584472656, 236.76913452148438, 329.91204833984375], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "2 Related Work", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14423370361328, 484.1204833984375, 303.3141784667969], "page": 3, "span": [0, 1161], "__ref_s3_data": null}], "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 4, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 532.7620849609375, 480.59576416015625, 673.0662231445312], "page": 4, "span": [0, 939], "__ref_s3_data": null}], "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 305.3533020019531, 480.595703125, 529.3430786132812], "page": 4, "span": [0, 1404], "__ref_s3_data": null}], "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 209.4513397216797, 480.5937805175781, 301.93426513671875], "page": 4, "span": [0, 572], "__ref_s3_data": null}], "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 175.88177490234375, 269.6244201660156, 186.45016479492188], "page": 4, "span": [0, 19], "__ref_s3_data": null}], "text": "3 Problem Statement", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14434051513672, 480.59368896484375, 159.85231018066406], "page": 4, "span": [0, 233], "__ref_s3_data": null}], "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 5, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 604.4931640625, 480.5937805175781, 673.0662231445312], "page": 5, "span": [0, 422], "__ref_s3_data": null}], "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.6070098876953, 562.7882080078125, 469.7522277832031, 570.9207153320312], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [134.76499938964844, 259.57940673828125, 480.5947570800781, 423.793212890625], "page": 5, "span": [0, 1021], "__ref_s3_data": null}], "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 211.29440307617188, 480.5928955078125, 255.95736694335938], "page": 5, "span": [0, 313], "__ref_s3_data": null}], "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 127.14539337158203, 480.5947265625, 207.67337036132812], "page": 5, "span": [0, 542], "__ref_s3_data": null}], "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 6, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.59478759765625, 673.0662231445312], "page": 6, "span": [0, 132], "__ref_s3_data": null}], "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 496.2580871582031, 480.595703125, 648.5172119140625], "page": 6, "span": [0, 977], "__ref_s3_data": null}], "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 460.8005065917969, 372.50848388671875, 471.368896484375], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "4 Optimised Table Structure Language", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 350.400146484375, 480.5947265625, 442.8830261230469], "page": 6, "span": [0, 563], "__ref_s3_data": null}], "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 317.3211364746094, 261.80108642578125, 326.1280822753906], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "4.1 Language Definition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 270.2941589355469, 480.5887145996094, 303.0021057128906], "page": 6, "span": [0, 165], "__ref_s3_data": null}], "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.708984375, 257.701171875, 409.3113708496094, 266.4981384277344], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.99298095703125, 235.22317504882812, 460.54443359375, 244.0301055908203], "page": 6, "span": [0, 72], "__ref_s3_data": null}], "text": "- -\"C\" cell a new table cell that either has or does not have cell content", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.99301147460938, 210.6751708984375, 480.59393310546875, 231.43710327148438], "page": 6, "span": [0, 82], "__ref_s3_data": null}], "text": "- -\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 186.1261749267578, 480.58856201171875, 206.8881072998047], "page": 6, "span": [0, 81], "__ref_s3_data": null}], "text": "- -\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 173.53317260742188, 454.5549621582031, 182.34010314941406], "page": 6, "span": [0, 71], "__ref_s3_data": null}], "text": "- -\"X\" cell cross cell , to merge with both left and upper neighbor cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.9930419921875, 160.93917846679688, 328.61676025390625, 169.74610900878906], "page": 6, "span": [0, 40], "__ref_s3_data": null}], "text": "- -\"NL\" new-line , switch to the next row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76504516601562, 127.14515686035156, 480.5928039550781, 147.8971405029297], "page": 6, "span": [0, 99], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 7, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "7", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 636.1503295898438, 480.58740234375, 666.2008056640625], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.76499938964844, 477.8972473144531, 246.6519775390625, 486.7041931152344], "page": 7, "span": [0, 19], "__ref_s3_data": null}], "text": "4.2 Language Syntax", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 457.95526123046875, 363.7961730957031, 466.7522277832031], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.97299194335938, 424.0662536621094, 480.5890197753906, 444.8291931152344], "page": 7, "span": [0, 108], "__ref_s3_data": null}], "text": "- 1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 400.15325927734375, 480.59228515625, 420.9151916503906], "page": 7, "span": [0, 106], "__ref_s3_data": null}], "text": "- 2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 388.19525146484375, 226.0736083984375, 397.002197265625], "page": 7, "span": [0, 20], "__ref_s3_data": null}], "text": "3. Cross cell rule :", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [151.70098876953125, 352.3262939453125, 480.5923767089844, 385.0332336425781], "page": 7, "span": [0, 167], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 340.3673095703125, 474.5901794433594, 349.17425537109375], "page": 7, "span": [0, 78], "__ref_s3_data": null}], "text": "- 4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 316.4543151855469, 480.58746337890625, 337.21624755859375], "page": 7, "span": [0, 84], "__ref_s3_data": null}], "text": "- 5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.97299194335938, 292.5403137207031, 480.5945739746094, 313.3032531738281], "page": 7, "span": [0, 144], "__ref_s3_data": null}], "text": "- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76498413085938, 151.05833435058594, 480.5958251953125, 279.40728759765625], "page": 7, "span": [0, 848], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 127.14533233642578, 480.5926513671875, 147.89730834960938], "page": 7, "span": [0, 153], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 139.37193298339844, 698.22900390625], "page": 8, "span": [0, 1], "__ref_s3_data": null}], "text": "8", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.8133544921875, 690.1593017578125, 231.72227478027344, 698.22900390625], "page": 8, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 652.314208984375, 480.5888366699219, 673.0662231445312], "page": 8, "span": [0, 84], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 621.63623046875, 319.3470764160156, 630.4431762695312], "page": 8, "span": [0, 35], "__ref_s3_data": null}], "text": "4.3 Error-detection and -mitigation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 493.32415771484375, 480.59576416015625, 609.7182006835938], "page": 8, "span": [0, 797], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 460.2676086425781, 229.03533935546875, 470.83599853515625], "page": 8, "span": [0, 13], "__ref_s3_data": null}], "text": "5 Experiments", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 340.3122863769531, 480.59527587890625, 444.7501525878906], "page": 8, "span": [0, 684], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.7650146484375, 288.2603454589844, 480.5908203125, 307.35186767578125], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59173583984375, 171.80722045898438], "page": 8, "span": [0, 299], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 9, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [475.98431396484375, 690.1593017578125, 480.59124755859375, 698.22900390625], "page": 9, "span": [0, 1], "__ref_s3_data": null}], "text": "9", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 640.3582153320312, 480.5957946777344, 673.0662231445312], "page": 9, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.0072021484375, 318.44842529296875, 622.8141479492188], "page": 9, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76498413085938, 537.8411254882812, 480.5927734375, 606.4141845703125], "page": 9, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76498413085938, 464.9591979980469, 480.59539794921875, 516.9276733398438], "page": 9, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.76499938964844, 275.041259765625, 264.4033203125, 283.84820556640625], "page": 9, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 174.9652557373047, 480.59576416015625, 267.44921875], "page": 9, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 127.1452407836914, 480.59576416015625, 171.80722045898438], "page": 9, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 10, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 646.1133422851562, 480.59356689453125, 676.163818359375], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.76499938964844, 494.27825927734375, 257.0867919921875, 503.085205078125], "page": 10, "span": [0, 23], "__ref_s3_data": null}], "text": "5.3 Qualitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 425.5223083496094, 480.5898132324219, 482.13922119140625], "page": 10, "span": [0, 309], "__ref_s3_data": null}], "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 352.2828369140625, 480.591064453125, 394.4098815917969], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [227.91465759277344, 116.65360260009766, 230.10028076171875, 126.1739730834961], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u03bc", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [300.58056640625, 98.57134246826172, 302.72637939453125, 108.3780517578125], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u2265", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 11, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 614.2323608398438, 480.58837890625, 666.2008056640625], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "12", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 12, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 663.8826293945312, 219.25479125976562, 674.4510498046875], "page": 12, "span": [0, 12], "__ref_s3_data": null}], "text": "6 Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 588.5181884765625, 480.595703125, 645.13623046875], "page": 12, "span": [0, 330], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 468.1632080078125, 480.59478759765625, 584.5562133789062], "page": 12, "span": [0, 724], "__ref_s3_data": null}], "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 323.8973388671875, 480.5948181152344, 464.201171875], "page": 12, "span": [0, 926], "__ref_s3_data": null}], "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.76499938964844, 287.61077880859375, 197.68641662597656, 298.1791687011719], "page": 12, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 228.12855529785156, 480.5920104980469, 269.1201477050781], "page": 12, "span": [0, 270], "__ref_s3_data": null}], "text": "- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 183.53439331054688, 480.5920104980469, 224.4811553955078], "page": 12, "span": [0, 301], "__ref_s3_data": null}], "text": "- 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 160.81239318847656, 480.5873107910156, 179.84115600585938], "page": 12, "span": [0, 140], "__ref_s3_data": null}], "text": "- 3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.3709716796875, 127.13239288330078, 480.5882568359375, 157.11915588378906], "page": 12, "span": [0, 204], "__ref_s3_data": null}], "text": "- 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [194.47799682617188, 690.1593017578125, 447.54290771484375, 698.22900390625], "page": 13, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [471.3756103515625, 690.1593017578125, 480.5894775390625, 698.22900390625], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "13", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [139.37100219726562, 642.3383178710938, 480.59478759765625, 672.3259887695312], "page": 13, "span": [0, 203], "__ref_s3_data": null}], "text": "- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 598.4913940429688, 480.5928649902344, 639.4380493164062], "page": 13, "span": [0, 264], "__ref_s3_data": null}], "text": "- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 576.5624389648438, 480.5901184082031, 595.5911254882812], "page": 13, "span": [0, 131], "__ref_s3_data": null}], "text": "- 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 521.7116088867188, 480.5947265625, 573.6611328125], "page": 13, "span": [0, 345], "__ref_s3_data": null}], "text": "- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.37100219726562, 488.8674621582031, 480.5938720703125, 518.8551635742188], "page": 13, "span": [0, 234], "__ref_s3_data": null}], "text": "- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 423.05767822265625, 480.5937194824219, 485.96722412109375], "page": 13, "span": [0, 413], "__ref_s3_data": null}], "text": "- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 379.2555236816406, 480.59295654296875, 420.2022705078125], "page": 13, "span": [0, 295], "__ref_s3_data": null}], "text": "- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 335.4085388183594, 480.5946960449219, 376.35528564453125], "page": 13, "span": [0, 281], "__ref_s3_data": null}], "text": "- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 291.5167236328125, 480.5937194824219, 332.50830078125], "page": 13, "span": [0, 275], "__ref_s3_data": null}], "text": "- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 247.7145538330078, 480.5928649902344, 288.66131591796875], "page": 13, "span": [0, 241], "__ref_s3_data": null}], "text": "- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 181.90472412109375, 480.5958251953125, 244.81431579589844], "page": 13, "span": [0, 405], "__ref_s3_data": null}], "text": "- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 160.0205535888672, 480.5954284667969, 179.04931640625], "page": 13, "span": [0, 96], "__ref_s3_data": null}], "text": "- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76400756835938, 127.13255310058594, 480.5911865234375, 157.1203155517578], "page": 13, "span": [0, 195], "__ref_s3_data": null}], "text": "- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 690.1593017578125, 143.97886657714844, 698.22900390625], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "14", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [167.82052612304688, 690.1593017578125, 231.72048950195312, 698.22900390625], "page": 14, "span": [0, 16], "__ref_s3_data": null}], "text": "M. Lysak, et al.", "type": "page-header", "payload": null, "name": "Page-header", "font": null}, {"prov": [{"bbox": [134.76499938964844, 642.3383178710938, 480.59112548828125, 672.3259887695312], "page": 14, "span": [0, 223], "__ref_s3_data": null}], "text": "- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76499938964844, 598.45751953125, 480.5946960449219, 639.4490356445312], "page": 14, "span": [0, 269], "__ref_s3_data": null}], "text": "- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.7649688720703, 576.5853881835938, 480.5935363769531, 595.6130981445312], "page": 14, "span": [0, 147], "__ref_s3_data": null}], "text": "- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 521.74560546875, 480.5930480957031, 573.6961059570312], "page": 14, "span": [0, 329], "__ref_s3_data": null}], "text": "- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 477.9544982910156, 480.5955810546875, 518.9011840820312], "page": 14, "span": [0, 259], "__ref_s3_data": null}], "text": "- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.76495361328125, 445.0785217285156, 480.59454345703125, 475.0652770996094], "page": 14, "span": [0, 206], "__ref_s3_data": null}], "text": "- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [148.45361328125, 366.1537780761719, 464.3608093261719, 583.625732421875], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [137.41452026367188, 451.7695007324219, 476.5608215332031, 558.4876708984375], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [164.65028381347656, 511.6590576171875, 449.5505676269531, 628.202880859375], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [140.7096710205078, 198.32275390625, 472.73382568359375, 283.93609619140625], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [162.67430114746094, 128.78643798828125, 451.70062255859375, 347.37744140625], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [168.3928985595703, 157.99432373046875, 447.3513488769531, 610.0335693359375], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [139.66845703125, 322.5279235839844, 475.00372314453125, 454.4252624511719], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 7, "data": [[{"bbox": [160.3699951171875, 442.1952819824219, 168.0479278564453, 450.2650451660156], "spans": [[0, 0]], "text": "#", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [207.9739990234375, 442.1952819824219, 215.6519317626953, 450.2650451660156], "spans": [[0, 1]], "text": "#", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [239.79800415039062, 436.7162780761719, 278.3176574707031, 444.7860412597656], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, 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diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
index fe3614c1..12de98a6 100644
--- a/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
+++ b/tests/data/groundtruth/docling_v1/2305.03393v1.pages.json
@@ -1 +1 @@
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"coord_origin": "TOPLEFT"}}, {"id": 39, "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "bbox": {"l": 222.96609, "t": 228.80853000000002, "r": 392.38983, "b": 236.27752999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.969645619392395, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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{"id": 1, "label": "text", "bbox": {"l": 163.1111, "t": 270.30115, "r": 452.24878000000007, "b": 464.73447, "coord_origin": "TOPLEFT"}, "confidence": 0.9807655215263367, "cells": [{"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.969645619392395, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899651169776917, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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"text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, 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452.31378, "r": 480.59232000000003, "b": 520.88669, "coord_origin": "TOPLEFT"}, "confidence": 0.9505022168159485, "cells": [{"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, "coord_origin": "TOPLEFT"}, "confidence": 0.968888521194458, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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"coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, "coord_origin": "TOPLEFT"}, "confidence": 0.968888521194458, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919057250022888, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919057250022888, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 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Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "picture", "bbox": {"l": 164.65020751953125, "t": 163.79708862304688, "r": 449.5506591796875, "b": 280.3410949707031, "coord_origin": "TOPLEFT"}, "confidence": 0.7868280410766602, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 202.39239999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "C", "bbox": {"l": 386.68707, "t": 205.13756999999998, "r": 393.86224, "b": 214.45934999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "C", "bbox": {"l": 398.65729, "t": 180.73279000000002, "r": 405.83246, "b": 190.05457, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 410.77908, "t": 180.73859000000004, "r": 417.95425, "b": 190.06035999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 422.90503, "t": 180.65247, "r": 430.08020000000005, "b": 189.97424, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 398.7807, "t": 192.98865, "r": 405.95587, "b": 202.31042000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 410.90164, "t": 192.99487, "r": 418.07681, "b": 202.31664999999998, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 423.02753, "t": 192.909, "r": 430.2027, "b": 202.23077, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 398.78235, "t": 205.31573000000003, "r": 405.95752, "b": 214.63751000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 410.90414, "t": 205.32196, "r": 418.07932, "b": 214.64373999999998, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 423.03003, "t": 205.23614999999995, "r": 430.20520000000005, "b": 214.55791999999997, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 386.50574, "t": 217.03882, "r": 393.68091, "b": 226.36059999999998, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 398.60181, "t": 217.21704, "r": 405.77698, "b": 226.53882, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 410.72275, "t": 217.22321, "r": 417.89792, "b": 226.54498, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 422.84869, "t": 217.13738999999998, "r": 430.02386, "b": 226.45916999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "NL", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "NL", "bbox": {"l": 435.44415, "t": 180.20025999999996, "r": 448.14679, "b": 189.52202999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "NL", "bbox": {"l": 435.46735, "t": 192.49474999999995, "r": 448.16998000000007, "b": 201.81652999999994, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "NL", "bbox": {"l": 435.38202, "t": 204.83025999999995, "r": 448.08466, "b": 214.15204000000006, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "NL", "bbox": {"l": 435.59906, "t": 217.2337, "r": 448.3017, "b": 226.55548, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "U", "bbox": {"l": 374.14957, "t": 205.23492, "r": 381.32474, "b": 214.55669999999998, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "U", "bbox": {"l": 374.0419, "t": 217.14648, "r": 381.21707, "b": 226.46826, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "U", "bbox": {"l": 374.34418, "t": 180.93488000000002, "r": 381.51935, "b": 190.25665000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "L", "bbox": {"l": 387.76285, "t": 168.57788000000005, "r": 393.28833, "b": 177.89966000000004, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", "bbox": {"l": 411.86395, "t": 168.06195000000002, "r": 417.38943, "b": 177.38373, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "L", "bbox": {"l": 423.33563, "t": 167.93439, "r": 428.86111, "b": 177.25616000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "X", "bbox": {"l": 387.13593, "t": 180.78576999999996, "r": 393.76453, "b": 190.10753999999997, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 295.52902, "t": 244.49347, "r": 301.0545, "b": 253.81525, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 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"confidence": 1.0, "cells": [{"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 82, "label": "text", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 83, "label": "text", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 84, "label": "text", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 85, "label": "text", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169163107872009, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588111639022827, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585384726524353, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506180167198181, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247209548950195, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785565137863159, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169163107872009, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588111639022827, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585384726524353, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506180167198181, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247209548950195, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785565137863159, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65020751953125, "t": 163.79708862304688, "r": 449.5506591796875, "b": 280.3410949707031, "coord_origin": "TOPLEFT"}, "confidence": 0.7868280410766602, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 202.39239999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "C", "bbox": {"l": 386.68707, "t": 205.13756999999998, "r": 393.86224, "b": 214.45934999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "C", "bbox": {"l": 398.65729, "t": 180.73279000000002, "r": 405.83246, "b": 190.05457, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 410.77908, "t": 180.73859000000004, "r": 417.95425, "b": 190.06035999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 422.90503, "t": 180.65247, "r": 430.08020000000005, "b": 189.97424, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 398.7807, "t": 192.98865, "r": 405.95587, "b": 202.31042000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 410.90164, "t": 192.99487, "r": 418.07681, "b": 202.31664999999998, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 423.02753, "t": 192.909, "r": 430.2027, "b": 202.23077, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 398.78235, "t": 205.31573000000003, "r": 405.95752, "b": 214.63751000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 410.90414, "t": 205.32196, "r": 418.07932, "b": 214.64373999999998, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 423.03003, "t": 205.23614999999995, "r": 430.20520000000005, "b": 214.55791999999997, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 386.50574, "t": 217.03882, "r": 393.68091, "b": 226.36059999999998, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 398.60181, "t": 217.21704, "r": 405.77698, "b": 226.53882, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 410.72275, "t": 217.22321, "r": 417.89792, "b": 226.54498, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 422.84869, "t": 217.13738999999998, "r": 430.02386, "b": 226.45916999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "NL", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "NL", "bbox": {"l": 435.44415, "t": 180.20025999999996, "r": 448.14679, "b": 189.52202999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "NL", "bbox": {"l": 435.46735, "t": 192.49474999999995, "r": 448.16998000000007, "b": 201.81652999999994, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "NL", "bbox": {"l": 435.38202, "t": 204.83025999999995, "r": 448.08466, "b": 214.15204000000006, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "NL", "bbox": {"l": 435.59906, "t": 217.2337, "r": 448.3017, "b": 226.55548, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "U", "bbox": {"l": 374.14957, "t": 205.23492, "r": 381.32474, "b": 214.55669999999998, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "U", "bbox": {"l": 374.0419, "t": 217.14648, "r": 381.21707, "b": 226.46826, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "U", "bbox": {"l": 374.34418, 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250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 51, "text": "C", "bbox": {"l": 250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 65, "label": "text", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 66, "label": "text", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 67, "label": "text", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 68, "label": "text", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 69, "label": "text", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 70, "label": "text", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 71, "label": "text", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 72, "label": "text", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250747799873352, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 73, "label": "text", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 75, "label": "text", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 76, "label": "text", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 78, "label": "text", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 79, "label": "text", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 80, "label": "text", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 81, "label": "text", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 82, "label": "text", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 83, "label": "text", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 84, "label": "text", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 85, "label": "text", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169163107872009, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588111639022827, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585384726524353, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506180167198181, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247209548950195, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785565137863159, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 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526.68796, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 327.04089, "t": 528.51508, "r": 329.5679, "b": 532.77826, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "3", "bbox": {"l": 424.14102, "t": 527.4428399999999, "r": 426.66803, "b": 531.7060200000001, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "2", "bbox": {"l": 453.0018, "t": 517.4539500000001, "r": 455.52881, "b": 521.71713, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "1", "bbox": {"l": 423.85825, "t": 517.06281, "r": 426.38525, "b": 521.32599, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "C", "bbox": {"l": 333.4342, "t": 557.36679, "r": 337.27542, "b": 562.35719, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "C", "bbox": {"l": 340.35397, "t": 557.31679, "r": 344.19519, "b": 562.30719, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "C", "bbox": {"l": 340.30978, "t": 563.8653899999999, "r": 344.151, "b": 568.8557900000001, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "C", "bbox": {"l": 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"coord_origin": "TOPLEFT"}}, {"id": 101, "text": "L", "bbox": {"l": 347.37979, "t": 557.08235, "r": 350.33786, "b": 562.07275, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3", "bbox": {"l": 331.14026, "t": 564.2907700000001, "r": 333.66727, "b": 568.55399, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "2", "bbox": {"l": 340.80972, "t": 554.59312, "r": 343.33673, "b": 558.85634, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "1", "bbox": {"l": 330.97992, "t": 554.83035, "r": 333.50693, "b": 559.09357, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301319479942322, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.863182783126831, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 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prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301319479942322, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.863182783126831, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096405029297, "t": 508.06390380859375, "r": 472.7337951660156, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096405029297, "t": 508.06390380859375, "r": 472.7337951660156, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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"original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. 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Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. 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"text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.93733811378479, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.885762631893158, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.985123336315155, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.985123336315155, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.93733811378479, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.885762631893158, "cells": [{"id": 1, "text": "9", "bbox": {"l": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482715129852295, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.6731414794922, "t": 444.6224365234375, "r": 451.7003479003906, "b": 663.213623046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9742222428321838, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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"section_header", "bbox": {"l": 134.765, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9546456933021545, "cells": [{"id": 55, "text": "5.3", "bbox": {"l": 134.765, "t": 288.91479, "r": 149.40205, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3 Qualitative Results"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58981, "b": 366.47769, "coord_origin": "TOPLEFT"}, "confidence": 0.9834066033363342, "cells": [{"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482715129852295, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.6731414794922, "t": 444.6224365234375, "r": 451.7003479003906, "b": 663.213623046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9742222428321838, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39266967773438, "t": 181.9679718017578, "r": 447.3527526855469, "b": 634.0032958984375, "coord_origin": "TOPLEFT"}, "confidence": 0.7615764737129211, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39266967773438, "t": 181.9679718017578, "r": 447.3527526855469, "b": 634.0032958984375, "coord_origin": "TOPLEFT"}, "confidence": 0.7615764737129211, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": [{"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301633238792419, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007189273834229, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610870242118835, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730682373046875, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610870242118835, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730682373046875, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.93040531873703, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899644017219543, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, "r": 414.10443, "b": 311.89532, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 407.56335, "t": 314.40619, "r": 414.12564, "b": 322.9317, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 418.51108, "t": 292.08502000000004, "r": 425.07336, "b": 300.61053000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 429.59744, "t": 292.09106, "r": 436.1597300000001, "b": 300.61658, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 440.68759000000006, "t": 292.01230000000004, "r": 447.24987999999996, "b": 300.53781000000004, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 418.6232, "t": 303.29483, "r": 425.18549, "b": 311.82034, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 429.7095299999999, "t": 303.30011, "r": 436.27182, "b": 311.82562, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 440.7996800000001, "t": 303.22211, "r": 447.36197, "b": 311.74762, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 418.62546, "t": 314.56903, "r": 425.18774, "b": 323.09454, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "C", "bbox": {"l": 429.71181999999993, "t": 314.57434, "r": 436.27411, "b": 323.09985, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "C", "bbox": {"l": 440.80194, "t": 314.49631, "r": 447.36423, "b": 323.02182, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "C", "bbox": {"l": 407.39746, "t": 325.29031, "r": 413.95975, "b": 333.81583, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "C", "bbox": {"l": 418.45959, "t": 325.45316, "r": 425.02188, "b": 333.97867, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "C", "bbox": {"l": 429.54593, "t": 325.4592, "r": 436.10822, "b": 333.98471, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "C", "bbox": {"l": 440.63608, "t": 325.38043, "r": 447.19836, "b": 333.90594, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "NL", "bbox": {"l": 451.89511000000005, "t": 280.15717, "r": 463.51273000000003, "b": 288.68268, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "NL", "bbox": {"l": 452.1557, "t": 291.59875000000005, "r": 463.77332, "b": 300.12427, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "NL", "bbox": {"l": 452.17688000000004, "t": 302.84265, "r": 463.79449000000005, "b": 311.36816, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "NL", "bbox": {"l": 452.09887999999995, "t": 314.12441999999993, "r": 463.71648999999996, "b": 322.6499299999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "NL", "bbox": {"l": 452.29733, "t": 325.46906, "r": 463.91495, "b": 333.99457, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "U", "bbox": {"l": 396.09677, "t": 314.49478, "r": 402.65906, "b": 323.02029000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "U", "bbox": {"l": 395.99829, "t": 325.38876000000005, "r": 402.56058, "b": 333.91428, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 396.27475, "t": 292.27057, "r": 402.83704, "b": 300.79608, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "L", "bbox": {"l": 408.54724, "t": 280.96912, "r": 413.60074, "b": 289.49463, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 430.58966, "t": 280.49725, "r": 435.6431600000001, "b": 289.02277, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 441.08069, "t": 280.38062, "r": 446.13419, "b": 288.90613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "X", "bbox": {"l": 407.97388, "t": 292.13425, "r": 414.03625, "b": 300.65976, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "NL", "bbox": {"l": 441.25640999999996, "t": 411.1807600000001, "r": 452.87402, "b": 419.7062700000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "vocabulary:", "bbox": {"l": 393.75256, "t": 399.7947700000001, "r": 432.48929, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "5", "bbox": {"l": 434.5896000000001, "t": 399.7947700000001, "r": 438.80083999999994, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "tokens", "bbox": {"l": 440.90573, "t": 399.7947700000001, "r": 463.22235, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "D OTSL", "bbox": {"l": 384.11816, "t": 258.54718, "r": 413.99307, "b": 265.65179, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "sequence length:", "bbox": {"l": 393.75256, "t": 266.67505000000006, "r": 451.45129000000003, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "30", "bbox": {"l": 453.55083999999994, "t": 266.67505000000006, "r": 461.97485, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vocabulary for this table:", "bbox": {"l": 151.79318, "t": 399.76016, "r": 233.89371000000003, "b": 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"<td", "bbox": {"l": 249.37506000000002, "t": 409.77362, "r": 259.03918, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "colspan=\"2\"", "bbox": {"l": 262.82797, "t": 409.77362, "r": 298.93646, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "colspan=\"3\"", "bbox": {"l": 302.72385, "t": 409.77362, "r": 338.83234, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rowspan=\"2\"", "bbox": {"l": 152.36208, "t": 418.10522, "r": 190.74123, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "rowspan=\"3\"", "bbox": {"l": 194.52863, "t": 418.10522, "r": 232.90777999999997, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": ">", "bbox": {"l": 236.69518999999997, "t": 418.10522, "r": 240.67617999999996, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "</table>", "bbox": {"l": 244.46358, "t": 418.10522, "r": 269.10144, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "C", "bbox": {"l": 154.50595, "t": 258.60095, "r": 159.62473, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "HTML", "bbox": {"l": 164.74348, "t": 258.60095, "r": 185.21857, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "sequence length:", "bbox": {"l": 164.3548, "t": 266.49707, "r": 222.05352999999997, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "55", "bbox": {"l": 224.15326, "t": 266.49707, "r": 232.57729, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45361328125, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845513701438904, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45361328125, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, "r": 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On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845513701438904, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45361328125, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, "r": 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"coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845513701438904, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439422488212585, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325800538063049, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443227767944, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290871620178223, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393904209136963, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.78900545835495, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844798445701599, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41452026367188, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387974739074707, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.926919162273407, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696111440658569, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378866910934448, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919180631637573, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856826663017273, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9570288062095642, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555868268013, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319928884506226, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438968896865845, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036383628845215, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636868000030518, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443336129188538, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378866910934448, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919180631637573, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856826663017273, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9570288062095642, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555868268013, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319928884506226, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438968896865845, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036383628845215, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636868000030518, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443336129188538, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856826663017273, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9570288062095642, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555868268013, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319928884506226, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438968896865845, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036383628845215, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636868000030518, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443336129188538, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378866910934448, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919180631637573, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", "bbox": {"l": 411.86395, "t": 168.06195000000002, "r": 417.38943, "b": 177.38373, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "L", "bbox": {"l": 423.33563, "t": 167.93439, "r": 428.86111, "b": 177.25616000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "X", "bbox": {"l": 387.13593, "t": 180.78576999999996, "r": 393.76453, "b": 190.10753999999997, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 295.52902, "t": 244.49347, "r": 301.0545, "b": 253.81525, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "C", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "C", "bbox": {"l": 250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326512813568115, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732971191406, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333082437515259, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, 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"coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420597553253174, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.961783230304718, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326512813568115, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732971191406, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333082437515259, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 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{"id": 70, "label": "text", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 71, "label": "text", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 72, "label": "text", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250620245933533, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 73, "label": "text", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 75, "label": "text", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 76, "label": "text", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 78, "label": "text", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 79, "label": "text", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 80, "label": "text", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 81, "label": "text", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 82, "label": "text", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 83, "label": "text", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 84, "label": "text", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 85, "label": "text", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.923302412033081, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169169068336487, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174802780151, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247233390808105, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420597553253174, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.961783230304718, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333082437515259, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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"bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.958811342716217, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174802780151, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247233390808105, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420597553253174, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.961783230304718, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326512813568115, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732971191406, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 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the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301324844360352, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631827235221863, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). 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Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096710205078, "t": 508.06390380859375, "r": 472.73382568359375, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303393959999084, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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"original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 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Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464746713638306, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515273571014404, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297955632209778, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096710205078, "t": 508.06390380859375, "r": 472.73382568359375, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303393959999084, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 347.21396, "r": 278.31766, "b": 355.28372, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 341.73495, "r": 348.26419, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 341.73495, "r": 417.12683, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 352.69394000000005, "r": 418.47278, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 341.73495, "r": 467.1423, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "time (secs)", "bbox": {"l": 427.14801, "t": 352.69394000000005, "r": 470.76056, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "simple", "bbox": {"l": 286.686, "t": 354.68594, "r": 312.33261, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "complex", "bbox": {"l": 320.702, "t": 354.68594, "r": 353.71988, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "all", "bbox": {"l": 369.306, "t": 354.68594, "r": 379.03094, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "6", "bbox": {"l": 161.90601, "t": 373.51596, "r": 166.51294, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "6", "bbox": {"l": 209.509, "t": 373.51596, "r": 214.11594, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 368.03595, "r": 271.40527, "b": 376.10571, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "0.965", "bbox": {"l": 289.017, "t": 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"TOPLEFT"}}, {"id": 46, "text": "0.955", "bbox": {"l": 363.67599, "t": 380.98795, "r": 384.66275, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "0.857", "bbox": {"l": 396.20599, "t": 380.98795, "r": 417.19275, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "5.39", "bbox": {"l": 440.767, "t": 380.98795, "r": 457.14682, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4", "bbox": {"l": 161.90601, "t": 399.81696, "r": 166.51294, "b": 407.88672, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4", "bbox": {"l": 209.509, "t": 399.81696, "r": 214.11594, "b": 407.88672, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 394.33795, "r": 271.40527, "b": 402.40771, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "0.938", "bbox": {"l": 289.017, "t": 394.33795, "r": 310.00375, "b": 402.40771, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.904", "bbox": {"l": 326.71701, "t": 394.33795, "r": 347.70377, 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396.20599, "t": 407.28894, "r": 417.19275, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3.77", "bbox": {"l": 440.767, "t": 407.28894, "r": 457.14682, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 161.90601, "t": 426.11795, "r": 166.51294, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "4", "bbox": {"l": 209.509, "t": 426.11795, "r": 214.11594, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 420.63895, "r": 271.40527, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "0.923", "bbox": {"l": 289.017, "t": 420.63895, "r": 310.00375, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0.897", "bbox": {"l": 326.71701, "t": 420.63895, "r": 347.70377, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "0.915", "bbox": {"l": 363.67599, "t": 420.63895, "r": 384.66275, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 420.57617, "r": 418.77887, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 420.57617, "r": 458.38425, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 433.58994, "r": 272.93954, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 433.58994, "r": 310.00375, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 433.58994, "r": 347.70377, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 433.5271599999999, "r": 386.24887, "b": 441.45346, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 433.58994, "r": 417.19275, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 433.58994, "r": 457.14682, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 452.41995, "r": 166.51294, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 452.41995, "r": 214.11594, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 446.9399399999999, "r": 271.40527, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 446.9399399999999, "r": 310.00375, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 446.9399399999999, "r": 345.40082, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 446.87717, "r": 386.24887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 446.87717, "r": 418.77887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": 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"text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579682350158691, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.957078218460083, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378157615662, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857628107070923, "cells": [{"id": 1, "text": "9", "bbox": 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more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.948272705078125, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.948272705078125, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.3928985595703, "t": 181.96646118164062, "r": 447.3513488769531, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": 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{"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301657676696777, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007200002670288, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610828518867493, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927666544914246, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873477220535278, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617196321487427, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.9754673838615417, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610828518867493, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927666544914246, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873477220535278, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617196321487427, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.9754673838615417, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873477220535278, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt b/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt
new file mode 100644
index 00000000..b6e4f1c5
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt
@@ -0,0 +1,25 @@
+<document>
+<paragraph><location><page_1><loc_12><loc_88><loc_53><loc_94></location>pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.</paragraph>
+<paragraph><location><page_1><loc_12><loc_77><loc_53><loc_86></location>The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.</paragraph>
+<subtitle-level-1><location><page_1><loc_12><loc_73><loc_28><loc_75></location>Boots Self-Locking Nut</subtitle-level-1>
+<paragraph><location><page_1><loc_12><loc_64><loc_54><loc_73></location>The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.</paragraph>
+<paragraph><location><page_1><loc_12><loc_52><loc_53><loc_62></location>The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.</paragraph>
+<paragraph><location><page_1><loc_12><loc_38><loc_54><loc_50></location>The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.</paragraph>
+<paragraph><location><page_1><loc_12><loc_33><loc_53><loc_36></location>Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is</paragraph>
+<caption><location><page_1><loc_12><loc_8><loc_31><loc_9></location>Figure 7-26. Self-locking nuts.</caption>
+<figure>
+<location><page_1><loc_12><loc_10><loc_52><loc_31></location>
+<caption>Figure 7-26. Self-locking nuts.</caption>
+</figure>
+<paragraph><location><page_1><loc_54><loc_85><loc_95><loc_94></location>the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.</paragraph>
+<paragraph><location><page_1><loc_54><loc_83><loc_55><loc_85></location>.</paragraph>
+<subtitle-level-1><location><page_1><loc_54><loc_82><loc_76><loc_83></location>Stainless Steel Self-Locking Nut</subtitle-level-1>
+<paragraph><location><page_1><loc_54><loc_54><loc_96><loc_81></location>The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.</paragraph>
+<subtitle-level-1><location><page_1><loc_54><loc_51><loc_65><loc_52></location>Elastic Stop Nut</subtitle-level-1>
+<paragraph><location><page_1><loc_54><loc_47><loc_93><loc_50></location>The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This</paragraph>
+<caption><location><page_1><loc_54><loc_8><loc_81><loc_10></location>Figure 7-27. Stainless steel self-locking nut.</caption>
+<figure>
+<location><page_1><loc_54><loc_11><loc_94><loc_46></location>
+<caption>Figure 7-27. Stainless steel self-locking nut.</caption>
+</figure>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.json b/tests/data/groundtruth/docling_v1/amt_handbook_sample.json
new file mode 100644
index 00000000..588b92f6
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.json
@@ -0,0 +1 @@
+{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "amt_handbook_sample.pdf", "filename-prov": null, "document-hash": "4ba7cdbd9ce8155d692d8f477f88bb3ec1acc2a463cf1e0209d1e624e58ebce9", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "f31706a847734c62e1e41f9f792c756283d1d4955552c1cc7f5e23c351bdd7cb", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [71.99212646484375, 681.3463745117188, 314.11212158203125, 730.3163452148438], "page": 1, "span": [0, 244], "__ref_s3_data": null}], "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99230194091797, 593.8463745117188, 313.15460205078125, 667.8163452148438], "page": 1, "span": [0, 376], "__ref_s3_data": null}], "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99230194091797, 568.8463745117188, 167.27230834960938, 580.1864013671875], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "Boots Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [71.99229431152344, 491.84637451171875, 318.49224853515625, 565.8163452148438], "page": 1, "span": [0, 319], "__ref_s3_data": null}], "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 404.34637451171875, 316.65728759765625, 478.3163757324219], "page": 1, "span": [0, 332], "__ref_s3_data": null}], "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 291.84637451171875, 318.8122863769531, 390.8163757324219], "page": 1, "span": [0, 477], "__ref_s3_data": null}], "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.99229431152344, 254.34637451171875, 313.91229248046875, 278.3163757324219], "page": 1, "span": [0, 122], "__ref_s3_data": null}], "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [72.0, 60.99040222167969, 184.14828491210938, 71.80239868164062], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. Self-locking nuts.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [320.9923095703125, 656.3463745117188, 561.808349609375, 730.3163452148438], "page": 1, "span": [0, 368], "__ref_s3_data": null}], "text": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.99542236328125, 643.8463745117188, 325.99542236328125, 655.3163452148438], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": ".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.99542236328125, 631.3463745117188, 450.99542236328125, 642.6864013671875], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "Stainless Steel Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.99542236328125, 416.84637451171875, 568.00439453125, 628.3163452148438], "page": 1, "span": [0, 1015], "__ref_s3_data": null}], "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.99542236328125, 391.84637451171875, 388.50543212890625, 403.1863708496094], "page": 1, "span": [0, 16], "__ref_s3_data": null}], "text": "Elastic Stop Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.99542236328125, 364.84637451171875, 552.351318359375, 388.8163757324219], "page": 1, "span": [0, 108], "__ref_s3_data": null}], "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [321.0, 63.01040267944336, 481.6493225097656, 73.82240295410156], "page": 1, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 7-27. Stainless steel self-locking nut.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [537.9854125976562, 33.70970153808594, 560.775390625, 46.01969909667969], "page": 1, "span": [0, 4], "__ref_s3_data": null}], "text": "7-45", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}], "figures": [{"prov": [{"bbox": [70.59269714355469, 79.6090087890625, 309.863037109375, 242.77777099609375], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. Self-locking nuts.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [320.4467468261719, 81.689208984375, 558.8576049804688, 352.359375], "page": 1, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 7-27. Stainless steel self-locking nut.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 774.0, "page": 1, "width": 594.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.md b/tests/data/groundtruth/docling_v1/amt_handbook_sample.md
new file mode 100644
index 00000000..d4cf5ec6
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.md
@@ -0,0 +1,31 @@
+pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.
+
+The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.
+
+## Boots Self-Locking Nut
+
+The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.
+
+The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.
+
+The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.
+
+Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is
+
+Figure 7-26. Self-locking nuts.
+<!-- image -->
+
+the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.
+
+.
+
+## Stainless Steel Self-Locking Nut
+
+The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.
+
+## Elastic Stop Nut
+
+The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This
+
+Figure 7-27. Stainless steel self-locking nut.
+<!-- image -->
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json b/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json
new file mode 100644
index 00000000..4bbc5e28
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json
@@ -0,0 +1 @@
+[{"page_no": 0, "size": {"width": 594.0, "height": 774.0}, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695364832878113, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253151416778564, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676452875137329, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": [{"id": 18, "label": "text", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "picture", "bbox": {"l": 70.59269714355469, "t": 531.2222290039062, "r": 309.863037109375, "b": 694.3909912109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9858751893043518, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497623443603516, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368571043014526, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695364832878113, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 \u2044 4 inch, the Rol-top ranges from 1 \u2044 4 inch to 1 \u2044 6 inch, and the bellows type ranges in size from No. 8 up to 3 \u2044 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253151416778564, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676452875137329, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 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"text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 70.59269714355469, "t": 531.2222290039062, "r": 309.863037109375, "b": 694.3909912109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9858751893043518, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497623443603516, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}, {"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368571043014526, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7-45"}], "body": [{"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695364832878113, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 \u2044 4 inch, the Rol-top ranges from 1 \u2044 4 inch to 1 \u2044 6 inch, and the bellows type ranges in size from No. 8 up to 3 \u2044 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253151416778564, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676452875137329, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": [{"id": 18, "label": "text", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 70.59269714355469, "t": 531.2222290039062, "r": 309.863037109375, "b": 694.3909912109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9858751893043518, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497623443603516, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368571043014526, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7-45"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.doctags.txt b/tests/data/groundtruth/docling_v1/code_and_formula.doctags.txt
new file mode 100644
index 00000000..1f1a0db7
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/code_and_formula.doctags.txt
@@ -0,0 +1,13 @@
+<document>
+<subtitle-level-1><location><page_1><loc_22><loc_83><loc_45><loc_84></location>Java Code Example</subtitle-level-1>
+<paragraph><location><page_1><loc_22><loc_63><loc_78><loc_81></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<paragraph><location><page_1><loc_39><loc_61><loc_61><loc_62></location>Listing 1: Simple Java Program</paragraph>
+<paragraph><location><page_1><loc_22><loc_56><loc_55><loc_60></location>public static void print() { System.out.println( "Java Code" ); }</paragraph>
+<paragraph><location><page_1><loc_22><loc_37><loc_78><loc_55></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<subtitle-level-1><location><page_2><loc_22><loc_84><loc_32><loc_85></location>Formula</subtitle-level-1>
+<paragraph><location><page_2><loc_22><loc_65><loc_80><loc_82></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<paragraph><location><page_2><loc_22><loc_58><loc_80><loc_65></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.</paragraph>
+<paragraph><location><page_2><loc_22><loc_38><loc_80><loc_55></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<paragraph><location><page_2><loc_22><loc_29><loc_80><loc_38></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.</paragraph>
+<paragraph><location><page_2><loc_22><loc_21><loc_80><loc_29></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.</paragraph>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.json b/tests/data/groundtruth/docling_v1/code_and_formula.json
new file mode 100644
index 00000000..8b90e794
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/code_and_formula.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.md b/tests/data/groundtruth/docling_v1/code_and_formula.md
new file mode 100644
index 00000000..4d77dc5c
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/code_and_formula.md
@@ -0,0 +1,19 @@
+## Java Code Example
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Listing 1: Simple Java Program
+
+public static void print() { System.out.println( "Java Code" ); }
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+## Formula
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.pages.json b/tests/data/groundtruth/docling_v1/code_and_formula.pages.json
new file mode 100644
index 00000000..27682fdd
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/code_and_formula.pages.json
@@ -0,0 +1 @@
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Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.850279688835144, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878177046775818, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844803214073181, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. 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Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711639046669006, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. 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Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt
new file mode 100644
index 00000000..cbcde73f
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt
@@ -0,0 +1,17 @@
+<document>
+<subtitle-level-1><location><page_1><loc_22><loc_83><loc_41><loc_84></location>Figures Example</subtitle-level-1>
+<paragraph><location><page_1><loc_22><loc_63><loc_78><loc_81></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<caption><location><page_1><loc_37><loc_32><loc_63><loc_33></location>Figure 1: This is an example image.</caption>
+<figure>
+<location><page_1><loc_22><loc_36><loc_78><loc_62></location>
+<caption>Figure 1: This is an example image.</caption>
+</figure>
+<paragraph><location><page_1><loc_22><loc_15><loc_78><loc_30></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.</paragraph>
+<paragraph><location><page_2><loc_22><loc_66><loc_78><loc_84></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</paragraph>
+<caption><location><page_2><loc_37><loc_33><loc_63><loc_34></location>Figure 2: This is an example image.</caption>
+<figure>
+<location><page_2><loc_36><loc_36><loc_64><loc_65></location>
+<caption>Figure 2: This is an example image.</caption>
+</figure>
+<paragraph><location><page_2><loc_22><loc_15><loc_78><loc_31></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.</paragraph>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/picture_classification.json b/tests/data/groundtruth/docling_v1/picture_classification.json
new file mode 100644
index 00000000..ad1bc5c2
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/picture_classification.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/picture_classification.md b/tests/data/groundtruth/docling_v1/picture_classification.md
new file mode 100644
index 00000000..6b9d1faf
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/picture_classification.md
@@ -0,0 +1,15 @@
+## Figures Example
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Figure 1: This is an example image.
+<!-- image -->
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Figure 2: This is an example image.
+<!-- image -->
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v1/picture_classification.pages.json b/tests/data/groundtruth/docling_v1/picture_classification.pages.json
new file mode 100644
index 00000000..744f54fb
--- /dev/null
+++ b/tests/data/groundtruth/docling_v1/picture_classification.pages.json
@@ -0,0 +1 @@
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Figures Example", "bbox": {"l": 133.76801, "t": 124.80877999999996, "r": 252.35513, "b": 137.54816000000005, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.67193999999995, "r": 477.47971, "b": 158.51873999999998, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.62694999999997, "r": 477.4806500000001, "b": 170.47375, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.58196999999996, "r": 477.47571000000005, "b": 182.42877, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 185.53698999999995, "r": 477.47466999999995, "b": 194.38378999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 197.49199999999996, "r": 477.47668, "b": 206.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 209.44701999999995, "r": 477.48068000000006, "b": 218.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 221.40301999999997, "r": 355.20874, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60098, "t": 221.40301999999997, "r": 477.48276, "b": 230.24982, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 233.35802999999999, "r": 477.47772, "b": 242.20483000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 245.31304999999998, "r": 477.47971, "b": 254.15985, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 257.26806999999997, "r": 477.47473, "b": 266.11487, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 269.22308, "r": 477.47571000000005, "b": 278.06989, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 281.17911, "r": 351.48471, "b": 290.02588, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 1: This is an example image.", "bbox": {"l": 226.89101, "t": 529.13495, "r": 384.3548, "b": 537.98174, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201, "t": 553.04495, "r": 477.47786999999994, "b": 561.89174, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801, "t": 564.99995, "r": 477.47860999999995, "b": 573.84674, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.json b/tests/data/groundtruth/docling_v1/redp5110_sampled.json
index 118f9599..4f35cf91 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.json
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.json
@@ -1 +1 @@
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All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.25927734375, 37.15127944946289], "page": 2, "span": [0, 3], "__ref_s3_data": null}], "text": "iii", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 706.416015625, 235.86239624023438, 717.5160522460938], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [94.13269805908203, 636.66357421875, 233.99972534179688, 653.5498657226562], "page": 3, "span": [0, 52], "__ref_s3_data": null}], "text": "Solution Brief IBM Systems Lab Services and Training", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [144.8892059326172, 455.1859436035156, 188.74681091308594, 464.5383605957031], "page": 3, "span": [0, 10], "__ref_s3_data": null}], "text": "Highlights", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [144.8892059326172, 433.3105773925781, 242.87388610839844, 446.7829284667969], "page": 3, "span": [0, 532], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g81>GLYPH<g75>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g72>GLYPH<g3> GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g73>GLYPH<g82>GLYPH<g85>GLYPH<g80>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g92>GLYPH<g82>GLYPH<g88>GLYPH<g85> GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 402.7626953125, 259.22869873046875, 424.06781005859375], "page": 3, "span": [0, 876], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g68>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g74>GLYPH<g85>GLYPH<g72>GLYPH<g68>GLYPH<g87>GLYPH<g72>GLYPH<g85>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g87>GLYPH<g88>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g55>GLYPH<g3> GLYPH<g83>GLYPH<g85>GLYPH<g82>GLYPH<g77>GLYPH<g72>GLYPH<g70>GLYPH<g87>GLYPH<g86> GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g85>GLYPH<g82>GLYPH<g88>GLYPH<g74>GLYPH<g75>GLYPH<g3> GLYPH<g80>GLYPH<g82>GLYPH<g71>GLYPH<g72>GLYPH<g85> GLYPH<g81>GLYPH<g76>GLYPH<g93>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71> GLYPH<g3> GLYPH<g68>GLYPH<g83>GLYPH<g83>GLYPH<g79>GLYPH<g76>GLYPH<g70>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 380.0474548339844, 249.8356170654297, 393.5198059082031], "page": 3, "span": [0, 672], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g53>GLYPH<g72>GLYPH<g79>GLYPH<g92>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g37>GLYPH<g48>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g3> GLYPH<g70>GLYPH<g82>GLYPH<g81>GLYPH<g86>GLYPH<g88>GLYPH<g79>GLYPH<g87>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g15>GLYPH<g3> GLYPH<g86>GLYPH<g78>GLYPH<g76>GLYPH<g79>GLYPH<g79>GLYPH<g86> GLYPH<g3> GLYPH<g86>GLYPH<g75>GLYPH<g68>GLYPH<g85>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g81>GLYPH<g82>GLYPH<g90>GLYPH<g81>GLYPH<g3> GLYPH<g86>GLYPH<g72>GLYPH<g85>GLYPH<g89>GLYPH<g76>GLYPH<g70>GLYPH<g72>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 357.3323669433594, 234.2516326904297, 370.8047180175781], "page": 3, "span": [0, 613], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g55> GLYPH<g68>GLYPH<g78>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g71>GLYPH<g89>GLYPH<g68>GLYPH<g81>GLYPH<g87>GLYPH<g68>GLYPH<g74>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g68>GLYPH<g70>GLYPH<g70>GLYPH<g72>GLYPH<g86>GLYPH<g86>GLYPH<g3> GLYPH<g87>GLYPH<g82>GLYPH<g3> GLYPH<g68> GLYPH<g3> GLYPH<g90>GLYPH<g82>GLYPH<g85>GLYPH<g79>GLYPH<g71>GLYPH<g90>GLYPH<g76>GLYPH<g71>GLYPH<g72>GLYPH<g3> GLYPH<g86>GLYPH<g82>GLYPH<g88>GLYPH<g85>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g76>GLYPH<g86>GLYPH<g72>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [461.0885925292969, 646.5781860351562, 506.26177978515625, 653.5924682617188], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "Power Services", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 515.3794555664062, 463.8094177246094, 552.6573486328125], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 504.5404052734375, 483.29571533203125, 514.4097290039062], "page": 3, "span": [0, 49], "__ref_s3_data": null}], "text": "Expert help to achieve your business requirements", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 467.1043395996094, 443.2821044921875, 476.1183776855469], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "We build confident, satisfied clients", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 447.0404968261719, 488.1546630859375, 464.6240539550781], "page": 3, "span": [0, 122], "__ref_s3_data": null}], "text": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 427.2699890136719, 367.8602294921875, 434.6739807128906], "page": 3, "span": [0, 27], "__ref_s3_data": null}], "text": "Because no one else is IBM.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 366.77972412109375, 500.321044921875, 414.9019775390625], "page": 3, "span": [0, 318], "__ref_s3_data": null}], "text": "With combined experiences and direct access to development groups, we're the experts in IBM DB2\u00ae for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 345.1319274902344, 434.8320617675781, 354.1459655761719], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "Who we are, some of what we do", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 335.2477722167969, 434.56317138671875, 342.6517639160156], "page": 3, "span": [0, 46], "__ref_s3_data": null}], "text": "Global CoE engagements cover topics including:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 315.4777526855469, 401.5641174316406, 322.8817443847656], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Database performance and scalability", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 305.2950439453125, 424.9964599609375, 312.69903564453125], "page": 3, "span": [0, 44], "__ref_s3_data": null}], "text": "- r Advanced SQL knowledge and skills transfer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 295.1124572753906, 392.158447265625, 302.5164489746094], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "- r Business intelligence and analytics", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 284.92974853515625, 339.94354248046875, 292.333740234375], "page": 3, "span": [0, 15], "__ref_s3_data": null}], "text": "- r DB2 Web Query", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 274.7471618652344, 504.1931457519531, 282.1511535644531], "page": 3, "span": [0, 72], "__ref_s3_data": null}], "text": "- r Query/400 modernization for better reporting and analysis capabilities", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 264.564453125, 423.002197265625, 271.96844482421875], "page": 3, "span": [0, 43], "__ref_s3_data": null}], "text": "- r Database modernization and re-engineering", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 254.38186645507812, 399.6517333984375, 261.7858581542969], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Data-centric architecture and design", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 244.1992645263672, 466.77880859375, 251.60325622558594], "page": 3, "span": [0, 58], "__ref_s3_data": null}], "text": "- r Extremely large database and overcoming limits to growth", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 234.0165557861328, 382.2095642089844, 241.42054748535156], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "- r ISV education and enablement", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 695.9519653320312, 151.46160888671875, 718.1519775390625], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Preface", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79983520507812, 590.1392822265625, 547.3082275390625, 659.3513793945312], "page": 4, "span": [0, 469], "__ref_s3_data": null}], "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79986572265625, 532.1800537109375, 546.4656982421875, 577.3925170898438], "page": 4, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.1584777832031, 547.2366943359375, 471.37127685546875], "page": 4, "span": [0, 172], "__ref_s3_data": null}], "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 4, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.2503051757812, 37.15127944946289], "page": 4, "span": [0, 2], "__ref_s3_data": null}], "text": "xi", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [263.3995666503906, 275.1402587890625, 541.2507934570312, 416.3512268066406], "page": 4, "span": [0, 684], "__ref_s3_data": null}], "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [263.39959716796875, 111.162841796875, 541.2737426757812, 264.37347412109375], "page": 4, "span": [0, 726], "__ref_s3_data": null}], "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 488.9364013671875, 125.36660766601562, 503.69940185546875], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Authors", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [81.0, 517.019287109375, 115.13253021240234, 523.457275390625], "page": 5, "span": [0, 10], "__ref_s3_data": null}], "text": "Chapter 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [500.3999938964844, 661.8682861328125, 522.6177368164062, 698.831298828125], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 482.1217956542969, 547.3047485351562, 537.1136474609375], "page": 5, "span": [0, 36], "__ref_s3_data": null}], "text": "Securing and protecting IBM DB2 data", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79965209960938, 362.078857421875, 547.2540283203125, 443.2912902832031], "page": 5, "span": [0, 648], "__ref_s3_data": null}], "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80023193359375, 304.0598449707031, 527.206298828125, 349.27227783203125], "page": 5, "span": [0, 304], "__ref_s3_data": null}], "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 270.1002197265625, 547.1551513671875, 291.3130187988281], "page": 5, "span": [0, 122], "__ref_s3_data": null}], "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 253.06063842773438, 250.23167419433594, 262.2736511230469], "page": 5, "span": [0, 37], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Security fundamentals", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 241.0608367919922, 282.98114013671875, 250.27383422851562], "page": 5, "span": [0, 47], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Current state of IBM i security", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 229.06103515625, 264.8818664550781, 238.27403259277344], "page": 5, "span": [0, 43], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> DB2 for i security controls", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 67.21955871582031, 258.362548828125, 74.24993896484375], "page": 5, "span": [0, 35], "__ref_s3_data": null}], "text": "$^{1 }$http://www.idtheftcenter.org", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [136.8000030517578, 57.02824020385742, 234.05880737304688, 64.40973663330078], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "$^{2 }$http://www.ponemon.org /", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [541.6798706054688, 27.93828010559082, 547.2176513671875, 37.15127944946289], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 702.8963012695312, 267.40582275390625, 717.6593017578125], "page": 6, "span": [0, 25], "__ref_s3_data": null}], "text": "1.1 Security fundamentals", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 664.178466796875, 545.0048217773438, 685.3912963867188], "page": 6, "span": [0, 133], "__ref_s3_data": null}], "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 611.138916015625, 547.1642456054688, 656.8751220703125], "page": 6, "span": [0, 361], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 522.1602172851562, 547.2608642578125, 603.3721313476562], "page": 6, "span": [0, 587], "__ref_s3_data": null}], "text": "- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 505.180419921875, 541.9920043945312, 514.3934326171875], "page": 6, "span": [0, 90], "__ref_s3_data": null}], "text": "A security policy is what defines whether the system and its settings are secure (or not).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79930114746094, 416.139404296875, 547.1582641601562, 497.8750305175781], "page": 6, "span": [0, 573], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002166748047, 382.1797790527344, 535.3616943359375, 403.392578125], "page": 6, "span": [0, 179], "__ref_s3_data": null}], "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 338.936279296875, 323.3839111328125, 353.69927978515625], "page": 6, "span": [0, 35], "__ref_s3_data": null}], "text": "1.2 Current state of IBM i security", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 276.1588439941406, 547.3182373046875, 321.37127685546875], "page": 6, "span": [0, 306], "__ref_s3_data": null}], "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 206.1400604248047, 547.284423828125, 263.3522644042969], "page": 6, "span": [0, 405], "__ref_s3_data": null}], "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 112.12167358398438, 547.2832641601562, 193.33349609375], "page": 6, "span": [0, 640], "__ref_s3_data": null}], "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 6, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 7, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 639.2794189453125, 544.3033447265625, 720.4913330078125], "page": 7, "span": [0, 589], "__ref_s3_data": null}], "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 606.67724609375, 301.4690246582031, 618.665283203125], "page": 7, "span": [0, 37], "__ref_s3_data": null}], "text": "1.3.1 Existing row and column control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79998779296875, 535.2990112304688, 541.5673828125, 592.5112915039062], "page": 7, "span": [0, 377], "__ref_s3_data": null}], "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 477.27996826171875, 547.4407958984375, 522.492431640625], "page": 7, "span": [0, 340], "__ref_s3_data": null}], "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 431.2607727050781, 547.232666015625, 464.473388671875], "page": 7, "span": [0, 247], "__ref_s3_data": null}], "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 91.85700225830078, 316.447265625, 100.18199920654297], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"prov": [{"bbox": [64.80000305175781, 708.67724609375, 335.4955139160156, 720.665283203125], "page": 8, "span": [0, 38], "__ref_s3_data": null}], "text": "2.1.6 Change Function Usage CL command", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 685.2982788085938, 547.284423828125, 694.5112915039062], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 668.2587280273438, 301.5174865722656, 677.4717407226562], "page": 8, "span": [0, 49], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80099487304688, 656.2589111328125, 313.39776611328125, 665.471923828125], "page": 8, "span": [0, 51], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8009796142578, 644.2590942382812, 310.8171081542969, 653.4721069335938], "page": 8, "span": [0, 52], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.7999725341797, 610.2994995117188, 512.5380249023438, 631.5123291015625], "page": 8, "span": [0, 126], "__ref_s3_data": null}], "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80096435546875, 593.5487670898438, 441.59686279296875, 602.3235473632812], "page": 8, "span": [0, 61], "__ref_s3_data": null}], "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 560.6572875976562, 544.4754638671875, 572.6453247070312], "page": 8, "span": [0, 72], "__ref_s3_data": null}], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 525.2785034179688, 519.5179443359375, 546.4913330078125], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 504.11700439453125, 283.9680480957031, 512.4420166015625], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8000030517578, 318.2784729003906, 547.2803955078125, 339.49127197265625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 297.11700439453125, 462.35418701171875, 305.4420166015625], "page": 8, "span": [0, 74], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8, 279.56719999999996, 171.26956, 288.34198], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "SELECT", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [182.75941, 279.56719999999996, 251.69853, 288.34198], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "function_id,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [166.78244, 267.56737999999996, 241.73852999999997, 276.3421599999999], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [170.75961, 255.56758000000002, 221.69901999999996, 264.34235], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "usage,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [167.53809, 243.56777999999997, 236.69878, 252.34253], "page": 8, "span": [0, 9], "__ref_s3_data": null}], "text": "user_type", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 231.56798000000003, 160.59396, 240.34272999999996], "page": 8, "span": [0, 4], "__ref_s3_data": null}], "text": "FROM", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [178.43944, 231.56798000000003, 261.71829, 240.34272999999996], "page": 8, "span": [0, 14], "__ref_s3_data": null}], "text": "function_usage", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 219.56817999999998, 162.44176, 228.34293000000002], "page": 8, "span": [0, 5], "__ref_s3_data": null}], "text": "WHERE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [177.8268, 219.56817999999998, 331.67731, 228.34293000000002], "page": 8, "span": [0, 28], "__ref_s3_data": null}], "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 207.56836999999996, 178.77542, 216.34312], "page": 8, "span": [0, 8], "__ref_s3_data": null}], "text": "ORDER BY", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [189.26929, 207.56836999999996, 241.73856, 216.34312], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 157.01637268066406, 249.59605407714844, 171.7793731689453], "page": 8, "span": [0, 24], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 82.23904418945312, 547.2234497070312, 139.45127868652344], "page": 8, "span": [0, 463], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 8, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 651.2788696289062, 542.6943359375, 720.490966796875], "page": 9, "span": [0, 516], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 593.2598266601562, 547.303955078125, 638.4722900390625], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 559.3002319335938, 538.6507568359375, 580.5130615234375], "page": 9, "span": [0, 129], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 513.281005859375, 545.7960205078125, 546.49365234375], "page": 9, "span": [0, 204], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 455.2619934082031, 539.80712890625, 500.47442626953125], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 421.3023681640625, 543.067138671875, 442.5151672363281], "page": 9, "span": [0, 136], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 400.1369934082031, 391.754638671875, 408.4620056152344], "page": 9, "span": [0, 78], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [355.32000732421875, 28.136999130249023, 523.5407104492188, 36.461997985839844], "page": 9, "span": [0, 41], "__ref_s3_data": null}], "text": "Chapter 2. Roles and separation of duties", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 9, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.799560546875, 699.2781372070312, 528.7305908203125, 720.490966796875], "page": 10, "span": [0, 135], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8000030517578, 369.5369873046875, 341.9765930175781, 377.86199951171875], "page": 10, "span": [0, 42], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8000030517578, 340.95599365234375, 215.37600708007812, 352.0559997558594], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 291.6988525390625, 542.7664794921875, 336.9112854003906], "page": 10, "span": [0, 297], "__ref_s3_data": null}], "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 10, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.2779541015625, 412.20758056640625, 720.490966796875], "page": 11, "span": [0, 62], "__ref_s3_data": null}], "text": "Table 3-1 summarizes these special registers and their values.", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 690.177001953125, 372.6036376953125, 698.501953125], "page": 11, "span": [0, 58], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [136.8000030517578, 556.2984619140625, 538.493896484375, 577.5112915039062], "page": 11, "span": [0, 97], "__ref_s3_data": null}], "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 539.2589111328125, 411.36138916015625, 548.471923828125], "page": 11, "span": [0, 75], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 522.2791137695312, 453.2580871582031, 531.4921264648438], "page": 11, "span": [0, 77], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 493.29949951171875, 541.4498291015625, 514.5123291015625], "page": 11, "span": [0, 160], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 452.2602844238281, 547.2167358398438, 485.472900390625], "page": 11, "span": [0, 253], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80101013183594, 423.2806701660156, 547.3540649414062, 444.49346923828125], "page": 11, "span": [0, 133], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 186.95709228515625, 341.2566223144531, 195.2821044921875], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.80000305175781, 154.457275390625, 247.02536010742188, 166.44528198242188], "page": 11, "span": [0, 31], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 119.0784683227539, 518.0011596679688, 140.29127502441406], "page": 11, "span": [0, 161], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 73.05928039550781, 532.3385009765625, 106.27189636230469], "page": 11, "span": [0, 233], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 11, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "19", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "20", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 12, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 342.5477294921875, 720.4913330078125], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 690.177001953125, 201.1814727783203, 698.501953125], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.80000305175781, 455.0362854003906, 384.3638916015625, 469.7992858886719], "page": 12, "span": [0, 34], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 356.2593994140625, 547.2347412109375, 437.4712829589844], "page": 12, "span": [0, 576], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 310.2999572753906, 547.2573852539062, 343.5125732421875], "page": 12, "span": [0, 235], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 288.2803955078125, 458.44525146484375, 297.4933776855469], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 271.2408142089844, 406.0775146484375, 280.45379638671875], "page": 12, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. There are user profiles for MGR, JANE, JUDY, and TONY.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 254.26100158691406, 396.9881591796875, 263.4739990234375], "page": 12, "span": [0, 58], "__ref_s3_data": null}], "text": "- 2. The user profile JANE specifies a group profile of MGR.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 225.28138732910156, 536.568603515625, 246.4941864013672], "page": 12, "span": [0, 127], "__ref_s3_data": null}], "text": "- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.20018005371094, 150.57144165039062, 451.01605224609375, 217.305419921875], "page": 12, "span": [0, 265], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.5667724609375, 166.73934936523438, 720.341552734375], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 699.5669555664062, 156.7793426513672, 708.3417358398438], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 531.5695190429688, 521.5742797851562, 696.3419189453125], "page": 13, "span": [0, 437], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 495.2812805175781, 547.2122192382812, 516.4940795898438], "page": 13, "span": [0, 136], "__ref_s3_data": null}], "text": "- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 478.3014831542969, 469.1528015136719, 487.51446533203125], "page": 13, "span": [0, 62], "__ref_s3_data": null}], "text": "- -Human Resources can see the unmasked TAX_ID of the employees.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 461.26190185546875, 403.95953369140625, 470.4748840332031], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "- -Employees can see only their own unmasked TAX_ID.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 432.28228759765625, 545.16845703125, 453.4950866699219], "page": 13, "span": [0, 129], "__ref_s3_data": null}], "text": "- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 415.302490234375, 529.463623046875, 424.5154724121094], "page": 13, "span": [0, 77], "__ref_s3_data": null}], "text": "- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.1997833251953, 398.2629089355469, 530.060302734375, 407.47589111328125], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 377.156982421875, 351.9873046875, 385.48199462890625], "page": 13, "span": [0, 48], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 107.55116271972656, 526.5546875, 368.3218994140625], "page": 13, "span": [0, 590], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 13, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "27", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 449.952392578125, 720.4913330078125], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 610.1370239257812, 293.1380920410156, 618.4619750976562], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.80000305175781, 577.6372680664062, 203.98521423339844, 589.6253051757812], "page": 14, "span": [0, 21], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 530.2586669921875, 547.2256469726562, 563.4713134765625], "page": 14, "span": [0, 265], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 513.2788696289062, 409.4788818359375, 522.4918823242188], "page": 14, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. Run the SQL statements that are shown in Example 3-10.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 492.11700439453125, 375.2909851074219, 500.4420166015625], "page": 14, "span": [0, 51], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 474.5671081542969, 376.6766052246094, 483.3418884277344], "page": 14, "span": [0, 45], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 462.5672912597656, 354.86962890625, 471.3420715332031], "page": 14, "span": [0, 39], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [365.77313232421875, 462.5672912597656, 376.6766052246094, 471.3420715332031], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "*/", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.5674743652344, 291.7178039550781, 459.3422546386719], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 438.5676574707031, 271.6783142089844, 447.3424377441406], "page": 14, "span": [0, 27], "__ref_s3_data": null}], "text": "ACTIVATE ROW ACCESS CONTROL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 426.5678405761719, 291.7178039550781, 435.3426208496094], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 378.27978515625, 540.8014526367188, 411.4924011230469], "page": 14, "span": [0, 231], "__ref_s3_data": null}], "text": "- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 134.63710021972656, 347.4305419921875, 142.9621124267578], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "28", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 687.2783203125, 514.048583984375, 720.490966796875], "page": 15, "span": [0, 228], "__ref_s3_data": null}], "text": "- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 303.11700439453125, 327.0932922363281, 311.4420166015625], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [136.8000030517578, 252.21875, 547.2394409179688, 285.4313659667969], "page": 15, "span": [0, 232], "__ref_s3_data": null}], "text": "- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 116.15709686279297, 227.1014862060547, 124.48210144042969], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [214.8000030517578, 28.136999130249023, 523.5935668945312, 36.461997985839844], "page": 15, "span": [0, 70], "__ref_s3_data": null}], "text": "Chapter 4. Implementing Row and Column Access Control: Banking example", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "77", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80030822753906, 85.39237976074219, 500.697265625, 720.3270263671875], "page": 16, "span": [0, 1998], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 83.98200225830078, 37.15127944946289], "page": 16, "span": [0, 3], "__ref_s3_data": null}], "text": "124", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [98.94000244140625, 28.136999130249023, 339.819580078125, 36.461997985839844], "page": 16, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [287.2200012207031, 741.251953125, 414.24481201171875, 763.4519653320312], "page": 18, "span": [0, 10], "__ref_s3_data": null}], "text": "Back cover", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [27.0, 651.5399780273438, 447.3600158691406, 718.3619995117188], "page": 18, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.700000762939453, 525.1680297851562, 127.443603515625, 549.8280029296875], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Implement roles and separation of duties", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 469.1280212402344, 120.283203125, 507.8280334472656], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Leverage row permissions on the database", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 413.14801025390625, 121.44960021972656, 451.8480224609375], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Protect columns by defining column masks", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94000244140625, 468.4081115722656, 414.084228515625, 549.2714233398438], "page": 18, "span": [0, 464], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.9400177001953, 403.4290466308594, 414.173828125, 460.292724609375], "page": 18, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [171.0, 152.3369903564453, 231.8876953125, 160.66200256347656], "page": 18, "span": [0, 12], "__ref_s3_data": null}], "text": "REDP-5110-00", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.3399963378906, 489.8393859863281, 559.809326171875, 544.2816772460938], "page": 18, "span": [0, 44], "__ref_s3_data": null}], "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 405.52801513671875, 587.38916015625, 440.2080078125], "page": 18, "span": [0, 60], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 250.36593627929688, 587.5205078125, 392.13970947265625], "page": 18, "span": [0, 323], "__ref_s3_data": null}], "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 190.48809814453125, 570.947998046875, 213.1680908203125], "page": 18, "span": [0, 39], "__ref_s3_data": null}], "text": "For more information: ibm.com /redbooks", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1807861328125, 586.1583251953125, 765.9149169921875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.090763092041016, 89.54693603515625, 585.1502075195312, 498.96728515625], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.94049072265625, 17.5740966796875, 581.354736328125, 81.87213134765625], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.5625762939453, 521.7388916015625], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.16698455810547, 103.87176513671875, 258.7742614746094, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.5288543701172, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.41448974609375, 156.61700439453125, 252.0884246826172, 264.75537109375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.05502700805664, 553.9589233398438, 239.62698364257812, 721.57373046875], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92469787597656, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.9717559814453, 381.3906555175781, 545.4180297851562, 684.5892944335938], "page": 10, "span": [0, 177], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64842224121094, 197.24334716796875, 301.23675537109375, 407.82635498046875], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.80186462402344, 621.9678955078125, 547.1146850585938, 696.6175537109375], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.98520278930664, 145.86041259765625, 530.0477905273438, 364.0950927734375], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.50160217285156, 314.4587097167969, 545.4507446289062, 672.7509155273438], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27841186523438, 127.9129638671875, 506.392578125, 238.41864013671875], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1697998046875, 737.8084106445312, 566.2962036132812, 766.7407836914062], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873779296875, 592.2726440429688, 711.9486083984375], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.1510467529297, 76.3472900390625, 547.5270385742188, 659.9696655273438], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 2, "#-rows": 43, "data": [[{"bbox": [136.8000030517578, 650.1383666992188, 172.89404296875, 659.3513793945312], "spans": [[0, 0]], "text": "Notices", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [175.01951599121094, 650.1383666992188, 547.1898193359375, 659.3513793945312], "spans": [[0, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [136.79901123046875, 637.6585083007812, 189.86537170410156, 646.8715209960938], "spans": [[1, 0]], "text": "Trademarks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [195.3968505859375, 637.6585083007812, 547.182861328125, 646.8715209960938], "spans": [[1, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [136.79901123046875, 615.1588745117188, 279.3973083496094, 624.3718872070312], "spans": [[2, 0]], "text": "DB2 for i Center of Excellence", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [280.6194152832031, 615.1588745117188, 547.1907958984375, 624.3718872070312], "spans": [[2, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [136.79901123046875, 592.6592407226562, 172.84423828125, 601.8722534179688], "spans": [[3, 0]], "text": "Preface", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [175.01852416992188, 592.6592407226562, 547.182861328125, 601.8722534179688], "spans": [[3, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [136.79803466796875, 580.1793823242188, 547.1808471679688, 589.3923950195312], "spans": [[4, 0]], "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [136.79803466796875, 567.6397705078125, 339.18292236328125, 576.852783203125], "spans": [[5, 0]], "text": "Now you can become a published author, too!", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [344.714111328125, 567.6397705078125, 547.1387939453125, 576.852783203125], "spans": [[5, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [136.79803466796875, 555.159912109375, 529.9950561523438, 564.3729248046875], "spans": [[6, 0]], "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [535.5494995117188, 555.159912109375, 547.1978759765625, 564.3729248046875], "spans": [[6, 1]], "text": "xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [136.79806518554688, 542.6800537109375, 284.0286560058594, 551.89306640625], "spans": [[7, 0]], "text": "Stay connected to IBM Redbooks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [289.54449462890625, 542.6800537109375, 547.1211547851562, 551.89306640625], "spans": [[7, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [136.79806518554688, 520.180419921875, 536.0958862304688, 529.3934326171875], "spans": [[8, 0]], "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [541.6468505859375, 520.180419921875, 547.1978149414062, 529.3934326171875], "spans": [[8, 1]], "text": "1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [136.79808044433594, 508.18060302734375, 549.8472290039062, 517.3936157226562], "spans": [[9, 0]], "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [136.79806518554688, 495.6409606933594, 536.1293334960938, 504.85394287109375], "spans": [[10, 0]], "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [541.6611328125, 495.6409606933594, 547.19287109375, 504.85394287109375], "spans": [[10, 1]], "text": "2", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [136.79806518554688, 483.16107177734375, 549.8472290039062, 492.3740539550781], "spans": [[11, 0]], "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [151.19720458984375, 470.6811828613281, 536.0551147460938, 479.8941650390625], "spans": [[12, 0]], "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [541.6015014648438, 470.6811828613281, 547.14794921875, 479.8941650390625], "spans": [[12, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}], [{"bbox": [151.19720458984375, 458.14154052734375, 536.080078125, 467.3545227050781], "spans": [[13, 0]], "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 13, "row-header": false, "row-span": [13, 14]}, {"bbox": [541.635498046875, 458.14154052734375, 547.19091796875, 467.3545227050781], "spans": [[13, 1]], "text": "5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 13, "row-header": false, "row-span": [13, 14]}], [{"bbox": [136.7970428466797, 435.64190673828125, 536.0908813476562, 444.8548889160156], "spans": [[14, 0]], "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 14, "row-header": false, "row-span": [14, 15]}, {"bbox": [541.642822265625, 435.64190673828125, 547.1947631835938, 444.8548889160156], "spans": [[14, 1]], "text": "7", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 14, "row-header": false, "row-span": [14, 15]}], [{"bbox": [136.7970428466797, 423.64208984375, 536.1271362304688, 432.8550720214844], "spans": [[15, 0]], "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 15, "row-header": false, "row-span": [15, 16]}, {"bbox": [541.6658935546875, 423.64208984375, 547.2047119140625, 432.8550720214844], "spans": [[15, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 15, "row-header": false, "row-span": [15, 16]}], [{"bbox": [151.19720458984375, 411.1622009277344, 535.9526977539062, 420.37518310546875], "spans": [[16, 0]], "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 16, "row-header": false, "row-span": [16, 17]}, {"bbox": [541.5558471679688, 411.1622009277344, 547.1590576171875, 420.37518310546875], "spans": [[16, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 16, "row-header": false, "row-span": [16, 17]}], [{"bbox": [151.19720458984375, 398.68231201171875, 536.0410766601562, 407.8952941894531], "spans": [[17, 0]], "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 17, "row-header": false, "row-span": [17, 18]}, {"bbox": [541.595947265625, 398.68231201171875, 547.1508178710938, 407.8952941894531], "spans": [[17, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 17, "row-header": false, "row-span": [17, 18]}], [{"bbox": [151.19720458984375, 386.1426696777344, 536.0748901367188, 395.35565185546875], "spans": [[18, 0]], "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 18, "row-header": false, "row-span": [18, 19]}, {"bbox": [541.6302490234375, 386.1426696777344, 547.1856079101562, 395.35565185546875], "spans": [[18, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 18, "row-header": false, "row-span": [18, 19]}], [{"bbox": [151.19720458984375, 373.66278076171875, 411.2704772949219, 382.8757629394531], "spans": [[19, 0]], "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 19, "row-header": false, "row-span": [19, 20]}, {"bbox": [416.8177490234375, 373.66278076171875, 547.1786499023438, 382.8757629394531], "spans": [[19, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 19, "row-header": false, "row-span": [19, 20]}], [{"bbox": [151.19720458984375, 361.1828918457031, 536.035888671875, 370.3958740234375], "spans": [[20, 0]], "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 20, "row-header": false, "row-span": [20, 21]}, {"bbox": [541.5989379882812, 361.1828918457031, 547.1619262695312, 370.3958740234375], "spans": [[20, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 20, "row-header": false, "row-span": [20, 21]}], [{"bbox": [151.19720458984375, 348.64324951171875, 530.5731811523438, 357.8562316894531], "spans": [[21, 0]], "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 21, "row-header": false, "row-span": [21, 22]}, {"bbox": [536.1044311523438, 348.64324951171875, 547.1668701171875, 357.8562316894531], "spans": [[21, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 21, "row-header": false, "row-span": [21, 22]}], [{"bbox": [151.19720458984375, 336.1633605957031, 530.5352172851562, 345.3763427734375], "spans": [[22, 0]], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 22, "row-header": false, "row-span": [22, 23]}, {"bbox": [536.0755004882812, 336.1633605957031, 547.156005859375, 345.3763427734375], "spans": [[22, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 22, "row-header": false, "row-span": [22, 23]}], [{"bbox": [136.7970428466797, 323.6834716796875, 547.256591796875, 332.8964538574219], "spans": [[23, 0]], "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 23, "row-header": false, "row-span": [23, 24]}, {"bbox": null, "spans": [[23, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 23, "row-header": false, "row-span": [23, 24]}], [{"bbox": [136.79702758789062, 301.183837890625, 530.5396118164062, 310.3968200683594], "spans": [[24, 0]], "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 24, "row-header": false, "row-span": [24, 25]}, {"bbox": [536.0916748046875, 301.183837890625, 547.19580078125, 310.3968200683594], "spans": [[24, 1]], "text": "13", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 24, "row-header": false, "row-span": [24, 25]}], [{"bbox": [136.79702758789062, 289.18402099609375, 530.4808959960938, 298.3970031738281], "spans": [[25, 0]], "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 25, "row-header": false, "row-span": [25, 26]}, {"bbox": [536.04248046875, 289.18402099609375, 547.1657104492188, 298.3970031738281], "spans": [[25, 1]], "text": "14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 25, "row-header": false, "row-span": [25, 26]}], [{"bbox": [151.1971893310547, 276.6443786621094, 378.2078552246094, 285.85736083984375], "spans": [[26, 0]], "text": "3.1.1 Row permission and column mask definitions", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 26, "row-header": false, "row-span": [26, 27]}, {"bbox": [383.74713134765625, 276.6443786621094, 547.15576171875, 285.85736083984375], "spans": [[26, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 26, "row-header": false, "row-span": [26, 27]}], [{"bbox": [151.1971893310547, 264.16448974609375, 530.4347534179688, 273.3774719238281], "spans": [[27, 0]], "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 27, "row-header": false, "row-span": [27, 28]}, {"bbox": [535.9962158203125, 264.16448974609375, 547.1190795898438, 273.3774719238281], "spans": [[27, 1]], "text": "16", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 27, "row-header": false, "row-span": [27, 28]}], [{"bbox": [136.79702758789062, 251.6248321533203, 530.528076171875, 260.83782958984375], "spans": [[28, 0]], "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 28, "row-header": false, "row-span": [28, 29]}, {"bbox": [536.0670166015625, 251.6248321533203, 547.1448364257812, 260.83782958984375], "spans": [[28, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 28, "row-header": false, "row-span": [28, 29]}], [{"bbox": [151.1971893310547, 239.14495849609375, 530.4978637695312, 248.3579559326172], "spans": [[29, 0]], "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 29, "row-header": false, "row-span": [29, 30]}, {"bbox": [536.0518798828125, 239.14495849609375, 547.159912109375, 248.3579559326172], "spans": [[29, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 29, "row-header": false, "row-span": [29, 30]}], [{"bbox": [151.1971893310547, 226.6650848388672, 530.5602416992188, 235.87808227539062], "spans": [[30, 0]], "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 30, "row-header": false, "row-span": [30, 31]}, {"bbox": [536.09912109375, 226.6650848388672, 547.1768798828125, 235.87808227539062], "spans": [[30, 1]], "text": "19", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 30, "row-header": false, "row-span": [30, 31]}], [{"bbox": [136.79702758789062, 214.1254425048828, 530.5302734375, 223.33843994140625], "spans": [[31, 0]], "text": "3.3 VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 31, "row-header": false, "row-span": [31, 32]}, {"bbox": [536.0615234375, 214.1254425048828, 547.1240234375, 223.33843994140625], "spans": [[31, 1]], "text": "20", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 31, "row-header": false, "row-span": [31, 32]}], [{"bbox": [136.79702758789062, 201.64556884765625, 530.6299438476562, 210.8585662841797], "spans": [[32, 0]], "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 32, "row-header": false, "row-span": [32, 33]}, {"bbox": [536.1631469726562, 201.64556884765625, 547.2295532226562, 210.8585662841797], "spans": [[32, 1]], "text": "21", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 32, "row-header": false, "row-span": [32, 33]}], [{"bbox": [400.3206481933594, 189.1656951904297, 530.4835815429688, 198.37869262695312], "spans": [[33, 0]], "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 33, "row-header": false, "row-span": [33, 34]}, {"bbox": [536.0223999023438, 189.1656951904297, 547.10009765625, 198.37869262695312], "spans": [[33, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 33, "row-header": false, "row-span": [33, 34]}], [{"bbox": [136.79701232910156, 176.6260528564453, 530.5651245117188, 198.37869262695312], "spans": [[34, 0]], "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 34, "row-header": false, "row-span": [34, 35]}, {"bbox": [536.1119995117188, 176.6260528564453, 547.2057495117188, 185.83905029296875], "spans": [[34, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 34, "row-header": false, "row-span": [34, 35]}], [{"bbox": [151.19717407226562, 164.14617919921875, 530.4913940429688, 173.3591766357422], "spans": [[35, 0]], "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 35, "row-header": false, "row-span": [35, 36]}, {"bbox": [536.0463256835938, 164.14617919921875, 547.1561889648438, 173.3591766357422], "spans": [[35, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 35, "row-header": false, "row-span": [35, 36]}], [{"bbox": [151.19717407226562, 151.6663055419922, 530.5645751953125, 160.87930297851562], "spans": [[36, 0]], "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 36, "row-header": false, "row-span": [36, 37]}, {"bbox": [536.0960083007812, 151.6663055419922, 547.1587524414062, 160.87930297851562], "spans": [[36, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 36, "row-header": false, "row-span": [36, 37]}], [{"bbox": [151.19717407226562, 139.1266632080078, 530.5569458007812, 148.33966064453125], "spans": [[37, 0]], "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 37, "row-header": false, "row-span": [37, 38]}, {"bbox": [536.0881958007812, 139.1266632080078, 547.1507568359375, 148.33966064453125], "spans": [[37, 1]], "text": "24", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 37, "row-header": false, "row-span": [37, 38]}], [{"bbox": [151.19717407226562, 126.64678955078125, 530.5341186523438, 135.8597869873047], "spans": [[38, 0]], "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 38, "row-header": false, "row-span": [38, 39]}, {"bbox": [536.072998046875, 126.64678955078125, 547.15087890625, 135.8597869873047], "spans": [[38, 1]], "text": "25", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 38, "row-header": false, "row-span": [38, 39]}], [{"bbox": [151.19717407226562, 114.16690826416016, 339.4510498046875, 123.37991333007812], "spans": [[39, 0]], "text": "3.6.5 Defining and creating column masks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 39, "row-header": false, "row-span": [39, 40]}, {"bbox": [344.9899597167969, 114.16690826416016, 547.160888671875, 123.37991333007812], "spans": [[39, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 39, "row-header": false, "row-span": [39, 40]}], [{"bbox": [151.19717407226562, 101.62727355957031, 530.541015625, 110.84027099609375], "spans": [[40, 0]], "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 40, "row-header": false, "row-span": [40, 41]}, {"bbox": [536.087646484375, 101.62727355957031, 547.1808471679688, 110.84027099609375], "spans": [[40, 1]], "text": "28", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 40, "row-header": false, "row-span": [40, 41]}], [{"bbox": [151.19717407226562, 89.14738464355469, 530.5750732421875, 98.36038970947266], "spans": [[41, 0]], "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 41, "row-header": false, "row-span": [41, 42]}, {"bbox": [536.1066284179688, 89.14738464355469, 547.169677734375, 98.36038970947266], "spans": [[41, 1]], "text": "29", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 41, "row-header": false, "row-span": [41, 42]}], [{"bbox": [151.19717407226562, 76.6675033569336, 530.436279296875, 85.88050842285156], "spans": [[42, 0]], "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 42, "row-header": false, "row-span": [42, 43]}, {"bbox": [535.9984741210938, 76.6675033569336, 547.1228637695312, 85.88050842285156], "spans": [[42, 1]], "text": "32", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 42, "row-header": false, "row-span": [42, 43]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [135.52508544921875, 349.9494934082031, 545.87060546875, 502.2750244140625], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 3, "#-rows": 5, "data": [[{"bbox": [142.8000030517578, 487.1369934082031, 202.2449951171875, 495.4620056152344], "spans": [[0, 0]], "text": "Column name", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [216.8087921142578, 487.1369934082031, 257.210693359375, 495.4620056152344], "spans": [[0, 1]], "text": "Data type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [289.47479248046875, 487.1369934082031, 338.8946838378906, 495.4620056152344], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8000030517578, 468.1172790527344, 203.2322998046875, 476.4422912597656], "spans": [[1, 0]], "text": "FUNCTION_ID", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [216.785400390625, 468.1172790527344, 276.00360107421875, 476.4422912597656], "spans": [[1, 1]], "text": "VARCHAR(30)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [289.45770263671875, 468.1172790527344, 359.85394287109375, 476.4422912597656], "spans": [[1, 2]], "text": "ID of the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8000030517578, 449.156982421875, 198.66929626464844, 457.48199462890625], "spans": [[2, 0]], "text": "USER_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [216.74130249023438, 449.156982421875, 275.9234924316406, 457.48199462890625], "spans": [[2, 1]], "text": "VARCHAR(10)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [289.382080078125, 438.1166687011719, 515.0535888671875, 457.48199462890625], "spans": [[2, 2]], "text": "Name of the user profile that has a usage setting for this  function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.79998779296875, 419.1563720703125, 173.98318481445312, 427.48138427734375], "spans": [[3, 0]], "text": "USAGE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [216.773681640625, 419.1563720703125, 270.9797668457031, 427.48138427734375], "spans": [[3, 1]], "text": "VARCHAR(7)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [289.416259765625, 397.13604736328125, 539.1071166992188, 427.48138427734375], "spans": [[3, 2]], "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [142.8000030517578, 378.1163330078125, 196.2248992919922, 386.44134521484375], "spans": [[4, 0]], "text": "USER_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [216.75210571289062, 378.1163330078125, 270.99871826171875, 386.44134521484375], "spans": [[4, 1]], "text": "VARCHAR(5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [289.4316101074219, 356.15631103515625, 448.11962890625, 386.44134521484375], "spans": [[4, 2]], "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [64.4115219116211, 70.3922119140625, 547.3950805664062, 398.3864440917969], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 6, "#-rows": 13, "data": [[{"bbox": [70.80030059814453, 383.1567077636719, 119.78550720214844, 391.4817199707031], "spans": [[0, 0]], "text": "User action", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": true, "row-span": [0, 1]}, {"bbox": [424.93804931640625, 304.9800109863281, 433.2629699707031, 344.4774475097656], "spans": [[0, 1]], "text": "*JOBCTL", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [450.1380615234375, 304.9800109863281, 458.4629821777344, 390.3999328613281], "spans": [[0, 2]], "text": "QIBM_DB_SECADM", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [475.9383544921875, 304.9800109863281, 484.2632751464844, 390.465576171875], "spans": [[0, 3]], "text": "QIBM_DB_SQLADM", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [501.13836669921875, 304.9800109863281, 509.4632873535156, 390.385498046875], "spans": [[0, 4]], "text": "QIBM_DB_SYSMON", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [526.3986206054688, 304.9799499511719, 534.7235717773438, 359.2005615234375], "spans": [[0, 5]], "text": "No Authority", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 285.11700439453125, 220.1568145751953, 293.4420166015625], "spans": [[1, 0]], "text": "SET CURRENT DEGREE  (SQL statement)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}, {"bbox": [429.0, 285.11700439453125, 435.00299072265625, 293.4420166015625], "spans": [[1, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [480.00030517578125, 285.11700439453125, 486.0032958984375, 293.4420166015625], "spans": [[1, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 266.1567077636719, 264.5538024902344, 274.4817199707031], "spans": [[2, 0]], "text": "CHGQRYA  command targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [429.0000305175781, 266.1567077636719, 435.0030212402344, 274.4817199707031], "spans": [[2, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [480.0003356933594, 266.1567077636719, 486.0033264160156, 274.4817199707031], "spans": [[2, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.800048828125, 247.1370086669922, 322.5057373046875, 255.46202087402344], "spans": [[3, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [429.0000305175781, 247.1370086669922, 435.0030212402344, 255.46202087402344], "spans": [[3, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [480.0003356933594, 247.1370086669922, 486.0033264160156, 255.46202087402344], "spans": [[3, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.800048828125, 228.1173095703125, 381.0218505859375, 236.44232177734375], "spans": [[4, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [429.0000305175781, 228.1173095703125, 435.0030212402344, 236.44232177734375], "spans": [[4, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [480.0003356933594, 228.1173095703125, 486.0033264160156, 236.44232177734375], "spans": [[4, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [505.2606201171875, 228.1173095703125, 511.26361083984375, 236.44232177734375], "spans": [[4, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [530.7603149414062, 228.1173095703125, 536.7633056640625, 236.44232177734375], "spans": [[4, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.800048828125, 209.15701293945312, 359.5173645019531, 217.48202514648438], "spans": [[5, 0]], "text": "QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [429.00006103515625, 209.15701293945312, 435.0030517578125, 217.48202514648438], "spans": [[5, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [480.0003662109375, 209.15701293945312, 486.00335693359375, 217.48202514648438], "spans": [[5, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [505.26068115234375, 209.15701293945312, 511.263671875, 217.48202514648438], "spans": [[5, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80007934570312, 190.13731384277344, 220.7517852783203, 198.4623260498047], "spans": [[6, 0]], "text": "Visual Explain within Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [429.00006103515625, 190.13731384277344, 435.0030517578125, 198.4623260498047], "spans": [[6, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": null, "spans": [[6, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [480.0003662109375, 190.13731384277344, 486.00335693359375, 198.4623260498047], "spans": [[6, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [505.26068115234375, 190.13731384277344, 511.263671875, 198.4623260498047], "spans": [[6, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [530.7603759765625, 190.13731384277344, 536.7633666992188, 198.4623260498047], "spans": [[6, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80007934570312, 171.11761474609375, 236.65480041503906, 179.442626953125], "spans": [[7, 0]], "text": "Visual Explain outside of Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}, {"bbox": [429.00006103515625, 171.11761474609375, 435.0030517578125, 179.442626953125], "spans": [[7, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [480.0003662109375, 171.11761474609375, 486.00335693359375, 179.442626953125], "spans": [[7, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.80007934570312, 152.15731811523438, 213.1296844482422, 160.48233032226562], "spans": [[8, 0]], "text": "ANALYZE PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}, {"bbox": [429.00006103515625, 152.15731811523438, 435.0030517578125, 160.48233032226562], "spans": [[8, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [480.0003662109375, 152.15731811523438, 486.00335693359375, 160.48233032226562], "spans": [[8, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [70.80007934570312, 133.1376190185547, 199.87808227539062, 141.46263122558594], "spans": [[9, 0]], "text": "DUMP PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [429.00006103515625, 133.1376190185547, 435.0030517578125, 141.46263122558594], "spans": [[9, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [480.0003662109375, 133.1376190185547, 486.00335693359375, 141.46263122558594], "spans": [[9, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [70.80007934570312, 114.11792755126953, 208.36776733398438, 122.44291687011719], "spans": [[10, 0]], "text": "MODIFY PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}, {"bbox": [429.00006103515625, 114.11792755126953, 435.0030517578125, 122.44291687011719], "spans": [[10, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [480.0003662109375, 114.11792755126953, 486.00335693359375, 122.44291687011719], "spans": [[10, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [70.80007934570312, 95.09822845458984, 411.20263671875, 103.42323303222656], "spans": [[11, 0]], "text": "MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}, {"bbox": [429.00006103515625, 95.09822845458984, 435.0030517578125, 103.42323303222656], "spans": [[11, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": [480.0003662109375, 95.09822845458984, 486.00335693359375, 103.42323303222656], "spans": [[11, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [70.80007934570312, 76.13793182373047, 377.1258544921875, 84.46292877197266], "spans": [[12, 0]], "text": "CHANGE PLAN CACHE SIZE procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}, {"bbox": [429.00006103515625, 76.13793182373047, 435.0030517578125, 84.46292877197266], "spans": [[12, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [480.0003662109375, 76.13793182373047, 486.00335693359375, 84.46292877197266], "spans": [[12, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 12, "row-header": false, "row-span": [12, 13]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [134.54624938964844, 587.7283935546875, 542.0460815429688, 688.5811157226562], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "table", "payload": null, "#-cols": 2, "#-rows": 4, "data": [[{"bbox": [142.8000030517578, 673.1370239257812, 209.67091369628906, 681.4619750976562], "spans": [[0, 0]], "text": "Special register", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [230.18911743164062, 673.1370239257812, 319.9352722167969, 681.4619750976562], "spans": [[0, 1]], "text": "Corresponding value", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.80001831054688, 643.1364135742188, 212.7012176513672, 662.5016479492188], "spans": [[1, 0]], "text": "USER or SESSION_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [230.2197265625, 654.1766967773438, 467.9906921386719, 662.5016479492188], "spans": [[1, 1]], "text": "The effective user of the thread excluding adopted authority.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.80003356933594, 624.11669921875, 216.63963317871094, 632.441650390625], "spans": [[2, 0]], "text": "CURRENT_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [230.19813537597656, 613.13671875, 535.6508178710938, 632.441650390625], "spans": [[2, 1]], "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.8009033203125, 594.1170043945312, 209.73570251464844, 602.4419555664062], "spans": [[3, 0]], "text": "SYSTEM_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [230.24490356445312, 594.1170043945312, 425.64569091796875, 602.4419555664062], "spans": [[3, 1]], "text": "The authorization ID that initiated the connection.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [63.55625915527344, 495.77532958984375, 548.5687255859375, 687.76611328125], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "table", "payload": null, "#-cols": 3, "#-rows": 10, "data": [[{"bbox": [70.80000305175781, 673.1370239257812, 134.99070739746094, 681.4619750976562], "spans": [[0, 0]], "text": "Global variable", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [202.889404296875, 673.1370239257812, 223.34640502929688, 681.4619750976562], "spans": [[0, 1]], "text": "Type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [281.8247985839844, 673.1370239257812, 331.3428039550781, 681.4619750976562], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 654.1766967773438, 132.7209014892578, 662.5016479492188], "spans": [[1, 0]], "text": "CLIENT_HOST", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], 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[202.72471618652344, 559.1375122070312, 261.9825439453125, 567.4624633789062], "spans": [[6, 1]], "text": "VARCHAR(64)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [281.7492370605469, 559.1375122070312, 478.84381103515625, 567.4624633789062], "spans": [[6, 2]], "text": "Version identifier of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80001831054688, 540.1177978515625, 154.419921875, 548.4427490234375], "spans": [[7, 0]], "text": "ROUTINE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [202.79312133789062, 540.1177978515625, 267.0927429199219, 548.4427490234375], "spans": [[7, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": 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All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.25927734375, 37.15127944946289], "page": 2, "span": [0, 3], "__ref_s3_data": null}], "text": "iii", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 706.416015625, 235.86239624023438, 717.5160522460938], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [94.13269805908203, 636.66357421875, 233.99972534179688, 653.5498657226562], "page": 3, "span": [0, 52], "__ref_s3_data": null}], "text": "Solution Brief IBM Systems Lab Services and Training", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [144.8892059326172, 455.1859436035156, 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[144.8892059326172, 402.7626953125, 259.22869873046875, 424.06781005859375], "page": 3, "span": [0, 876], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g68>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g74>GLYPH<g85>GLYPH<g72>GLYPH<g68>GLYPH<g87>GLYPH<g72>GLYPH<g85>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g87>GLYPH<g88>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g55>GLYPH<g3> GLYPH<g83>GLYPH<g85>GLYPH<g82>GLYPH<g77>GLYPH<g72>GLYPH<g70>GLYPH<g87>GLYPH<g86> GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g85>GLYPH<g82>GLYPH<g88>GLYPH<g74>GLYPH<g75>GLYPH<g3> GLYPH<g80>GLYPH<g82>GLYPH<g71>GLYPH<g72>GLYPH<g85> GLYPH<g81>GLYPH<g76>GLYPH<g93>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71> GLYPH<g3> GLYPH<g68>GLYPH<g83>GLYPH<g83>GLYPH<g79>GLYPH<g76>GLYPH<g70>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 380.0474548339844, 249.8356170654297, 393.5198059082031], "page": 3, "span": [0, 672], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g53>GLYPH<g72>GLYPH<g79>GLYPH<g92>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g37>GLYPH<g48>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g3> GLYPH<g70>GLYPH<g82>GLYPH<g81>GLYPH<g86>GLYPH<g88>GLYPH<g79>GLYPH<g87>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g15>GLYPH<g3> GLYPH<g86>GLYPH<g78>GLYPH<g76>GLYPH<g79>GLYPH<g79>GLYPH<g86> GLYPH<g3> GLYPH<g86>GLYPH<g75>GLYPH<g68>GLYPH<g85>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g81>GLYPH<g82>GLYPH<g90>GLYPH<g81>GLYPH<g3> GLYPH<g86>GLYPH<g72>GLYPH<g85>GLYPH<g89>GLYPH<g76>GLYPH<g70>GLYPH<g72>GLYPH<g86>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [144.8892059326172, 357.3323669433594, 234.2516326904297, 370.8047180175781], "page": 3, "span": [0, 613], "__ref_s3_data": null}], "text": "- GLYPH<g115>GLYPH<g3> GLYPH<g55> GLYPH<g68>GLYPH<g78>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g71>GLYPH<g89>GLYPH<g68>GLYPH<g81>GLYPH<g87>GLYPH<g68>GLYPH<g74>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g68>GLYPH<g70>GLYPH<g70>GLYPH<g72>GLYPH<g86>GLYPH<g86>GLYPH<g3> GLYPH<g87>GLYPH<g82>GLYPH<g3> GLYPH<g68> GLYPH<g3> GLYPH<g90>GLYPH<g82>GLYPH<g85>GLYPH<g79>GLYPH<g71>GLYPH<g90>GLYPH<g76>GLYPH<g71>GLYPH<g72>GLYPH<g3> GLYPH<g86>GLYPH<g82>GLYPH<g88>GLYPH<g85>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g76>GLYPH<g86>GLYPH<g72>", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [461.0885925292969, 646.5781860351562, 506.26177978515625, 653.5924682617188], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "Power Services", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 515.3794555664062, 463.8094177246094, 552.6573486328125], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "DB2 for i Center of Excellence", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 504.5404052734375, 483.29571533203125, 514.4097290039062], "page": 3, "span": [0, 49], "__ref_s3_data": null}], "text": "Expert help to achieve your business requirements", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 467.1043395996094, 443.2821044921875, 476.1183776855469], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "We build confident, satisfied clients", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 447.0404968261719, 488.1546630859375, 464.6240539550781], "page": 3, "span": [0, 122], "__ref_s3_data": null}], "text": "No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 427.2699890136719, 367.8602294921875, 434.6739807128906], "page": 3, "span": [0, 27], "__ref_s3_data": null}], "text": "Because no one else is IBM.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 366.77972412109375, 500.321044921875, 414.9019775390625], "page": 3, "span": [0, 318], "__ref_s3_data": null}], "text": "With combined experiences and direct access to development groups, we're the experts in IBM DB2\u00ae for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 345.1319274902344, 434.8320617675781, 354.1459655761719], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "Who we are, some of what we do", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [280.2401123046875, 335.2477722167969, 434.56317138671875, 342.6517639160156], "page": 3, "span": [0, 46], "__ref_s3_data": null}], "text": "Global CoE engagements cover topics including:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [280.2401123046875, 315.4777526855469, 401.5641174316406, 322.8817443847656], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Database performance and scalability", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 305.2950439453125, 424.9964599609375, 312.69903564453125], "page": 3, "span": [0, 44], "__ref_s3_data": null}], "text": "- r Advanced SQL knowledge and skills transfer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 295.1124572753906, 392.158447265625, 302.5164489746094], "page": 3, "span": [0, 37], "__ref_s3_data": null}], "text": "- r Business intelligence and analytics", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 284.92974853515625, 339.94354248046875, 292.333740234375], "page": 3, "span": [0, 15], "__ref_s3_data": null}], "text": "- r DB2 Web Query", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 274.7471618652344, 504.1931457519531, 282.1511535644531], "page": 3, "span": [0, 72], "__ref_s3_data": null}], "text": "- r Query/400 modernization for better reporting and analysis capabilities", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 264.564453125, 423.002197265625, 271.96844482421875], "page": 3, "span": [0, 43], "__ref_s3_data": null}], "text": "- r Database modernization and re-engineering", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 254.38186645507812, 399.6517333984375, 261.7858581542969], "page": 3, "span": [0, 38], "__ref_s3_data": null}], "text": "- r Data-centric architecture and design", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 244.1992645263672, 466.77880859375, 251.60325622558594], "page": 3, "span": [0, 58], "__ref_s3_data": null}], "text": "- r Extremely large database and overcoming limits to growth", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [280.2401123046875, 234.0165557861328, 382.2095642089844, 241.42054748535156], "page": 3, "span": [0, 30], "__ref_s3_data": null}], "text": "- r ISV education and enablement", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 695.9519653320312, 151.46160888671875, 718.1519775390625], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Preface", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79983520507812, 590.1392822265625, 547.3082275390625, 659.3513793945312], "page": 4, "span": [0, 469], "__ref_s3_data": null}], "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79986572265625, 532.1800537109375, 546.4656982421875, 577.3925170898438], "page": 4, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.1584777832031, 547.2366943359375, 471.37127685546875], "page": 4, "span": [0, 172], "__ref_s3_data": null}], "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 4, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [538.8599853515625, 27.93828010559082, 547.2503051757812, 37.15127944946289], "page": 4, "span": [0, 2], "__ref_s3_data": null}], "text": "xi", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [263.3995666503906, 275.1402587890625, 541.2507934570312, 416.3512268066406], "page": 4, "span": [0, 684], "__ref_s3_data": null}], "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [263.39959716796875, 111.162841796875, 541.2737426757812, 264.37347412109375], "page": 4, "span": [0, 726], "__ref_s3_data": null}], "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 488.9364013671875, 125.36660766601562, 503.69940185546875], "page": 4, "span": [0, 7], "__ref_s3_data": null}], "text": "Authors", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [81.0, 517.019287109375, 115.13253021240234, 523.457275390625], "page": 5, "span": [0, 10], "__ref_s3_data": null}], "text": "Chapter 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [500.3999938964844, 661.8682861328125, 522.6177368164062, 698.831298828125], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 482.1217956542969, 547.3047485351562, 537.1136474609375], "page": 5, "span": [0, 36], "__ref_s3_data": null}], "text": "Securing and protecting IBM DB2 data", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79965209960938, 362.078857421875, 547.2540283203125, 443.2912902832031], "page": 5, "span": [0, 648], "__ref_s3_data": null}], "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80023193359375, 304.0598449707031, 527.206298828125, 349.27227783203125], "page": 5, "span": [0, 304], "__ref_s3_data": null}], "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 270.1002197265625, 547.1551513671875, 291.3130187988281], "page": 5, "span": [0, 122], "__ref_s3_data": null}], "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8002471923828, 253.06063842773438, 250.23167419433594, 262.2736511230469], "page": 5, "span": [0, 37], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Security fundamentals", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 241.0608367919922, 282.98114013671875, 250.27383422851562], "page": 5, "span": [0, 47], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Current state of IBM i security", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002471923828, 229.06103515625, 264.8818664550781, 238.27403259277344], "page": 5, "span": [0, 43], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> DB2 for i security controls", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 67.21955871582031, 258.362548828125, 74.24993896484375], "page": 5, "span": [0, 35], "__ref_s3_data": null}], "text": "$^{1 }$http://www.idtheftcenter.org", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [136.8000030517578, 57.02824020385742, 234.05880737304688, 64.40973663330078], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "$^{2 }$http://www.ponemon.org /", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [64.80000305175781, 28.136999130249023, 257.24334716796875, 36.461997985839844], "page": 5, "span": [0, 48], "__ref_s3_data": null}], "text": "' Copyright IBM Corp. 2014. All rights reserved.", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [541.6798706054688, 27.93828010559082, 547.2176513671875, 37.15127944946289], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 702.8963012695312, 267.40582275390625, 717.6593017578125], "page": 6, "span": [0, 25], "__ref_s3_data": null}], "text": "1.1 Security fundamentals", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 664.178466796875, 545.0048217773438, 685.3912963867188], "page": 6, "span": [0, 133], "__ref_s3_data": null}], "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 611.138916015625, 547.1642456054688, 656.8751220703125], "page": 6, "span": [0, 361], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 522.1602172851562, 547.2608642578125, 603.3721313476562], "page": 6, "span": [0, 587], "__ref_s3_data": null}], "text": "- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199462890625, 505.180419921875, 541.9920043945312, 514.3934326171875], "page": 6, "span": [0, 90], "__ref_s3_data": null}], "text": "A security policy is what defines whether the system and its settings are secure (or not).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79930114746094, 416.139404296875, 547.1582641601562, 497.8750305175781], "page": 6, "span": [0, 573], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8002166748047, 382.1797790527344, 535.3616943359375, 403.392578125], "page": 6, "span": [0, 179], "__ref_s3_data": null}], "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 338.936279296875, 323.3839111328125, 353.69927978515625], "page": 6, "span": [0, 35], "__ref_s3_data": null}], "text": "1.2 Current state of IBM i security", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 276.1588439941406, 547.3182373046875, 321.37127685546875], "page": 6, "span": [0, 306], "__ref_s3_data": null}], "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 206.1400604248047, 547.284423828125, 263.3522644042969], "page": 6, "span": [0, 405], "__ref_s3_data": null}], "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 112.12167358398438, 547.2832641601562, 193.33349609375], "page": 6, "span": [0, 640], "__ref_s3_data": null}], "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "2", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 6, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 72.8219985961914, 37.15127944946289], "page": 7, "span": [0, 1], "__ref_s3_data": null}], "text": "4", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [87.84030151367188, 28.136999130249023, 328.7253723144531, 36.461997985839844], "page": 7, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 639.2794189453125, 544.3033447265625, 720.4913330078125], "page": 7, "span": [0, 589], "__ref_s3_data": null}], "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 606.67724609375, 301.4690246582031, 618.665283203125], "page": 7, "span": [0, 37], "__ref_s3_data": null}], "text": "1.3.1 Existing row and column control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.79998779296875, 535.2990112304688, 541.5673828125, 592.5112915039062], "page": 7, "span": [0, 377], "__ref_s3_data": null}], "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 477.27996826171875, 547.4407958984375, 522.492431640625], "page": 7, "span": [0, 340], "__ref_s3_data": null}], "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79998779296875, 431.2607727050781, 547.232666015625, 464.473388671875], "page": 7, "span": [0, 247], "__ref_s3_data": null}], "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 91.85700225830078, 316.447265625, 100.18199920654297], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"prov": [{"bbox": [64.80000305175781, 708.67724609375, 335.4955139160156, 720.665283203125], "page": 8, "span": [0, 38], "__ref_s3_data": null}], "text": "2.1.6 Change Function Usage CL command", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 685.2982788085938, 547.284423828125, 694.5112915039062], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 668.2587280273438, 301.5174865722656, 677.4717407226562], "page": 8, "span": [0, 49], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80099487304688, 656.2589111328125, 313.39776611328125, 665.471923828125], "page": 8, "span": [0, 51], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8009796142578, 644.2590942382812, 310.8171081542969, 653.4721069335938], "page": 8, "span": [0, 52], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.7999725341797, 610.2994995117188, 512.5380249023438, 631.5123291015625], "page": 8, "span": [0, 126], "__ref_s3_data": null}], "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80096435546875, 593.5487670898438, 441.59686279296875, 602.3235473632812], "page": 8, "span": [0, 61], "__ref_s3_data": null}], "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 560.6572875976562, 544.4754638671875, 572.6453247070312], "page": 8, "span": [0, 72], "__ref_s3_data": null}], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 525.2785034179688, 519.5179443359375, 546.4913330078125], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 504.11700439453125, 283.9680480957031, 512.4420166015625], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8000030517578, 318.2784729003906, 547.2803955078125, 339.49127197265625], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 297.11700439453125, 462.35418701171875, 305.4420166015625], "page": 8, "span": [0, 74], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8, 279.56719999999996, 171.26956, 288.34198], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "SELECT", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [182.75941, 279.56719999999996, 251.69853, 288.34198], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "function_id,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [166.78244, 267.56737999999996, 241.73852999999997, 276.3421599999999], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [170.75961, 255.56758000000002, 221.69901999999996, 264.34235], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "usage,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [167.53809, 243.56777999999997, 236.69878, 252.34253], "page": 8, "span": [0, 9], "__ref_s3_data": null}], "text": "user_type", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 231.56798000000003, 160.59396, 240.34272999999996], "page": 8, "span": [0, 4], "__ref_s3_data": null}], "text": "FROM", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [178.43944, 231.56798000000003, 261.71829, 240.34272999999996], "page": 8, "span": [0, 14], "__ref_s3_data": null}], "text": "function_usage", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 219.56817999999998, 162.44176, 228.34293000000002], "page": 8, "span": [0, 5], "__ref_s3_data": null}], "text": "WHERE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [177.8268, 219.56817999999998, 331.67731, 228.34293000000002], "page": 8, "span": [0, 28], "__ref_s3_data": null}], "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 207.56836999999996, 178.77542, 216.34312], "page": 8, "span": [0, 8], "__ref_s3_data": null}], "text": "ORDER BY", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [189.26929, 207.56836999999996, 241.73856, 216.34312], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 157.01637268066406, 249.59605407714844, 171.7793731689453], "page": 8, "span": [0, 24], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 82.23904418945312, 547.2234497070312, 139.45127868652344], "page": 8, "span": [0, 463], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 8, "span": [0, 2], "__ref_s3_data": null}], "text": "10", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 8, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 651.2788696289062, 542.6943359375, 720.490966796875], "page": 9, "span": [0, 516], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 593.2598266601562, 547.303955078125, 638.4722900390625], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 559.3002319335938, 538.6507568359375, 580.5130615234375], "page": 9, "span": [0, 129], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 513.281005859375, 545.7960205078125, 546.49365234375], "page": 9, "span": [0, 204], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 455.2619934082031, 539.80712890625, 500.47442626953125], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 421.3023681640625, 543.067138671875, 442.5151672363281], "page": 9, "span": [0, 136], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 400.1369934082031, 391.754638671875, 408.4620056152344], "page": 9, "span": [0, 78], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [355.32000732421875, 28.136999130249023, 523.5407104492188, 36.461997985839844], "page": 9, "span": [0, 41], "__ref_s3_data": null}], "text": "Chapter 2. Roles and separation of duties", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 9, "span": [0, 2], "__ref_s3_data": null}], "text": "11", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.799560546875, 699.2781372070312, 528.7305908203125, 720.490966796875], "page": 10, "span": [0, 135], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8000030517578, 369.5369873046875, 341.9765930175781, 377.86199951171875], "page": 10, "span": [0, 42], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8000030517578, 340.95599365234375, 215.37600708007812, 352.0559997558594], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 291.6988525390625, 542.7664794921875, 336.9112854003906], "page": 10, "span": [0, 297], "__ref_s3_data": null}], "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 10, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 10, "span": [0, 2], "__ref_s3_data": null}], "text": "15", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.2779541015625, 412.20758056640625, 720.490966796875], "page": 11, "span": [0, 62], "__ref_s3_data": null}], "text": "Table 3-1 summarizes these special registers and their values.", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 690.177001953125, 372.6036376953125, 698.501953125], "page": 11, "span": [0, 58], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [136.8000030517578, 556.2984619140625, 538.493896484375, 577.5112915039062], "page": 11, "span": [0, 97], "__ref_s3_data": null}], "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 539.2589111328125, 411.36138916015625, 548.471923828125], "page": 11, "span": [0, 75], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 522.2791137695312, 453.2580871582031, 531.4921264648438], "page": 11, "span": [0, 77], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 493.29949951171875, 541.4498291015625, 514.5123291015625], "page": 11, "span": [0, 160], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 452.2602844238281, 547.2167358398438, 485.472900390625], "page": 11, "span": [0, 253], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80101013183594, 423.2806701660156, 547.3540649414062, 444.49346923828125], "page": 11, "span": [0, 133], "__ref_s3_data": null}], "text": "- GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 186.95709228515625, 341.2566223144531, 195.2821044921875], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.80000305175781, 154.457275390625, 247.02536010742188, 166.44528198242188], "page": 11, "span": [0, 31], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 119.0784683227539, 518.0011596679688, 140.29127502441406], "page": 11, "span": [0, 161], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 73.05928039550781, 532.3385009765625, 106.27189636230469], "page": 11, "span": [0, 233], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 11, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 11, "span": [0, 2], "__ref_s3_data": null}], "text": "19", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "20", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 12, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 342.5477294921875, 720.4913330078125], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.80000305175781, 690.177001953125, 201.1814727783203, 698.501953125], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.80000305175781, 455.0362854003906, 384.3638916015625, 469.7992858886719], "page": 12, "span": [0, 34], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 356.2593994140625, 547.2347412109375, 437.4712829589844], "page": 12, "span": [0, 576], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 310.2999572753906, 547.2573852539062, 343.5125732421875], "page": 12, "span": [0, 235], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 288.2803955078125, 458.44525146484375, 297.4933776855469], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.80001831054688, 271.2408142089844, 406.0775146484375, 280.45379638671875], "page": 12, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. There are user profiles for MGR, JANE, JUDY, and TONY.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 254.26100158691406, 396.9881591796875, 263.4739990234375], "page": 12, "span": [0, 58], "__ref_s3_data": null}], "text": "- 2. The user profile JANE specifies a group profile of MGR.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.80001831054688, 225.28138732910156, 536.568603515625, 246.4941864013672], "page": 12, "span": [0, 127], "__ref_s3_data": null}], "text": "- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.20018005371094, 150.57144165039062, 451.01605224609375, 217.305419921875], "page": 12, "span": [0, 265], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 711.5667724609375, 166.73934936523438, 720.341552734375], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 699.5669555664062, 156.7793426513672, 708.3417358398438], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.79959106445312, 531.5695190429688, 521.5742797851562, 696.3419189453125], "page": 13, "span": [0, 437], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.79959106445312, 495.2812805175781, 547.2122192382812, 516.4940795898438], "page": 13, "span": [0, 136], "__ref_s3_data": null}], "text": "- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 478.3014831542969, 469.1528015136719, 487.51446533203125], "page": 13, "span": [0, 62], "__ref_s3_data": null}], "text": "- -Human Resources can see the unmasked TAX_ID of the employees.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 461.26190185546875, 403.95953369140625, 470.4748840332031], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "- -Employees can see only their own unmasked TAX_ID.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 432.28228759765625, 545.16845703125, 453.4950866699219], "page": 13, "span": [0, 129], "__ref_s3_data": null}], "text": "- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.03939819335938, 415.302490234375, 529.463623046875, 424.5154724121094], "page": 13, "span": [0, 77], "__ref_s3_data": null}], "text": "- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.1997833251953, 398.2629089355469, 530.060302734375, 407.47589111328125], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 377.156982421875, 351.9873046875, 385.48199462890625], "page": 13, "span": [0, 48], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "paragraph", "payload": null, "name": "paragraph", "font": null}, {"prov": [{"bbox": [136.8000030517578, 107.55116271972656, 526.5546875, 368.3218994140625], "page": 13, "span": [0, 590], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [344.94000244140625, 28.136999130249023, 523.6016235351562, 36.461997985839844], "page": 13, "span": [0, 40], "__ref_s3_data": null}], "text": "Chapter 3. Row and Column Access Control", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "27", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.8000030517578, 711.2783203125, 449.952392578125, 720.4913330078125], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 610.1370239257812, 293.1380920410156, 618.4619750976562], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.80000305175781, 577.6372680664062, 203.98521423339844, 589.6253051757812], "page": 14, "span": [0, 21], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 530.2586669921875, 547.2256469726562, 563.4713134765625], "page": 14, "span": [0, 265], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 513.2788696289062, 409.4788818359375, 522.4918823242188], "page": 14, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. Run the SQL statements that are shown in Example 3-10.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 492.11700439453125, 375.2909851074219, 500.4420166015625], "page": 14, "span": [0, 51], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8000030517578, 474.5671081542969, 376.6766052246094, 483.3418884277344], "page": 14, "span": [0, 45], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 462.5672912597656, 354.86962890625, 471.3420715332031], "page": 14, "span": [0, 39], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [365.77313232421875, 462.5672912597656, 376.6766052246094, 471.3420715332031], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "*/", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 450.5674743652344, 291.7178039550781, 459.3422546386719], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 438.5676574707031, 271.6783142089844, 447.3424377441406], "page": 14, "span": [0, 27], "__ref_s3_data": null}], "text": "ACTIVATE ROW ACCESS CONTROL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 426.5678405761719, 291.7178039550781, 435.3426208496094], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8000030517578, 378.27978515625, 540.8014526367188, 411.4924011230469], "page": 14, "span": [0, 231], "__ref_s3_data": null}], "text": "- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 134.63710021972656, 347.4305419921875, 142.9621124267578], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 78.4020004272461, 37.15127944946289], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "28", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [93.42030334472656, 28.136999130249023, 334.4214172363281, 36.461997985839844], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [136.79959106445312, 687.2783203125, 514.048583984375, 720.490966796875], "page": 15, "span": [0, 228], "__ref_s3_data": null}], "text": "- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8000030517578, 303.11700439453125, 327.0932922363281, 311.4420166015625], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"prov": [{"bbox": [136.8000030517578, 252.21875, 547.2394409179688, 285.4313659667969], "page": 15, "span": [0, 232], "__ref_s3_data": null}], "text": "- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.80000305175781, 116.15709686279297, 227.1014862060547, 124.48210144042969], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [214.8000030517578, 28.136999130249023, 523.5935668945312, 36.461997985839844], "page": 15, "span": [0, 70], "__ref_s3_data": null}], "text": "Chapter 4. Implementing Row and Column Access Control: Banking example", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [536.0999755859375, 27.93828010559082, 547.2591552734375, 37.15127944946289], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "77", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [64.80030822753906, 85.39237976074219, 500.697265625, 720.3270263671875], "page": 16, "span": [0, 1998], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [64.80000305175781, 27.93828010559082, 83.98200225830078, 37.15127944946289], "page": 16, "span": [0, 3], "__ref_s3_data": null}], "text": "124", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [98.94000244140625, 28.136999130249023, 339.819580078125, 36.461997985839844], "page": 16, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"prov": [{"bbox": [287.2200012207031, 741.251953125, 414.24481201171875, 763.4519653320312], "page": 18, "span": [0, 10], "__ref_s3_data": null}], "text": "Back cover", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [27.0, 651.5399780273438, 447.3600158691406, 718.3619995117188], "page": 18, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.700000762939453, 525.1680297851562, 127.443603515625, 549.8280029296875], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Implement roles and separation of duties", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 469.1280212402344, 120.283203125, 507.8280334472656], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Leverage row permissions on the database", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.700000762939453, 413.14801025390625, 121.44960021972656, 451.8480224609375], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Protect columns by defining column masks", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94000244140625, 468.4081115722656, 414.084228515625, 549.2714233398438], "page": 18, "span": [0, 464], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.9400177001953, 403.4290466308594, 414.173828125, 460.292724609375], "page": 18, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [171.0, 152.3369903564453, 231.8876953125, 160.66200256347656], "page": 18, "span": [0, 12], "__ref_s3_data": null}], "text": "REDP-5110-00", "type": "page-footer", "payload": null, "name": "Page-footer", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.3399963378906, 489.8393859863281, 559.809326171875, 544.2816772460938], "page": 18, "span": [0, 44], "__ref_s3_data": null}], "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 405.52801513671875, 587.38916015625, 440.2080078125], "page": 18, "span": [0, 60], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 250.36593627929688, 587.5205078125, 392.13970947265625], "page": 18, "span": [0, 323], "__ref_s3_data": null}], "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.3399963378906, 190.48809814453125, 570.947998046875, 213.1680908203125], "page": 18, "span": [0, 39], "__ref_s3_data": null}], "text": "For more information: ibm.com /redbooks", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1808471679688, 586.1583251953125, 765.9149169921875], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.090599060058594, 89.5469970703125, 585.1502075195312, 498.9671630859375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.94049072265625, 17.57415771484375, 581.3547973632812, 81.8721923828125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.56256103515625, 521.7388916015625], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.16704559326172, 103.87176513671875, 258.77435302734375, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.52883911132812, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.41445922851562, 156.616943359375, 252.08840942382812, 264.7552490234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.075252532958984, 554.0420532226562, 239.620361328125, 721.422607421875], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92466735839844, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 43], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.9717559814453, 381.39068603515625, 545.4180908203125, 684.5892333984375], "page": 10, "span": [0, 177], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64837646484375, 197.24334716796875, 301.2367248535156, 407.8263244628906], "page": 11, "span": [0, 50], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.80192184448242, 621.9678955078125, 547.11474609375, 696.6176147460938], "page": 14, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.985130310058594, 145.86041259765625, 530.0478515625, 364.0950012207031], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.5016632080078, 314.45880126953125, 545.4508666992188, 672.7509155273438], "page": 15, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27847290039062, 127.91290283203125, 506.39263916015625, 238.41851806640625], "page": 15, "span": [0, 37], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1698303222656, 737.8084106445312, 566.2962036132812, 766.7407836914062], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873779296875, 592.2726440429688, 711.9486694335938], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.1495819091797, 76.34844970703125, 547.52685546875, 659.9669799804688], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 2, "#-rows": 43, "data": [[{"bbox": [136.8000030517578, 650.1383666992188, 172.89404296875, 659.3513793945312], "spans": [[0, 0]], "text": "Notices", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [175.01951599121094, 650.1383666992188, 547.1898193359375, 659.3513793945312], "spans": [[0, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [136.79901123046875, 637.6585083007812, 189.86537170410156, 646.8715209960938], "spans": [[1, 0]], "text": "Trademarks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [195.3968505859375, 637.6585083007812, 547.182861328125, 646.8715209960938], "spans": [[1, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [136.79901123046875, 615.1588745117188, 279.3973083496094, 624.3718872070312], "spans": [[2, 0]], "text": "DB2 for i Center of Excellence", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [280.6194152832031, 615.1588745117188, 547.1907958984375, 624.3718872070312], "spans": [[2, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [136.79901123046875, 592.6592407226562, 172.84423828125, 601.8722534179688], "spans": [[3, 0]], "text": "Preface", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [175.01852416992188, 592.6592407226562, 547.182861328125, 601.8722534179688], "spans": [[3, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [136.79803466796875, 580.1793823242188, 547.1808471679688, 589.3923950195312], "spans": [[4, 0]], "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [136.79803466796875, 567.6397705078125, 339.18292236328125, 576.852783203125], "spans": [[5, 0]], "text": "Now you can become a published author, too!", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [344.714111328125, 567.6397705078125, 547.1387939453125, 576.852783203125], "spans": [[5, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [136.79803466796875, 555.159912109375, 529.9950561523438, 564.3729248046875], "spans": [[6, 0]], "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [535.5494995117188, 555.159912109375, 547.1978759765625, 564.3729248046875], "spans": [[6, 1]], "text": "xiii", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [136.79806518554688, 542.6800537109375, 284.0286560058594, 551.89306640625], "spans": [[7, 0]], "text": "Stay connected to IBM Redbooks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [289.54449462890625, 542.6800537109375, 547.1211547851562, 551.89306640625], "spans": [[7, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [136.79806518554688, 520.180419921875, 536.0958862304688, 529.3934326171875], "spans": [[8, 0]], "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [541.6468505859375, 520.180419921875, 547.1978149414062, 529.3934326171875], "spans": [[8, 1]], "text": "1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [136.79808044433594, 508.18060302734375, 549.8472290039062, 517.3936157226562], "spans": [[9, 0]], "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [136.79806518554688, 495.6409606933594, 536.1293334960938, 504.85394287109375], "spans": [[10, 0]], "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [541.6611328125, 495.6409606933594, 547.19287109375, 504.85394287109375], "spans": [[10, 1]], "text": "2", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [136.79806518554688, 483.16107177734375, 549.8472290039062, 492.3740539550781], "spans": [[11, 0]], "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [151.19720458984375, 470.6811828613281, 536.0551147460938, 479.8941650390625], "spans": [[12, 0]], "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [541.6015014648438, 470.6811828613281, 547.14794921875, 479.8941650390625], "spans": [[12, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}], [{"bbox": [151.19720458984375, 458.14154052734375, 536.080078125, 467.3545227050781], "spans": [[13, 0]], "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 13, "row-header": false, "row-span": [13, 14]}, {"bbox": [541.635498046875, 458.14154052734375, 547.19091796875, 467.3545227050781], "spans": [[13, 1]], "text": "5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 13, "row-header": false, "row-span": [13, 14]}], [{"bbox": [136.7970428466797, 435.64190673828125, 536.0908813476562, 444.8548889160156], "spans": [[14, 0]], "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 14, "row-header": false, "row-span": [14, 15]}, {"bbox": [541.642822265625, 435.64190673828125, 547.1947631835938, 444.8548889160156], "spans": [[14, 1]], "text": "7", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 14, "row-header": false, "row-span": [14, 15]}], [{"bbox": [136.7970428466797, 423.64208984375, 536.1271362304688, 432.8550720214844], "spans": [[15, 0]], "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 15, "row-header": false, "row-span": [15, 16]}, {"bbox": [541.6658935546875, 423.64208984375, 547.2047119140625, 432.8550720214844], "spans": [[15, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 15, "row-header": false, "row-span": [15, 16]}], [{"bbox": [151.19720458984375, 411.1622009277344, 535.9526977539062, 420.37518310546875], "spans": [[16, 0]], "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 16, "row-header": false, "row-span": [16, 17]}, {"bbox": [541.5558471679688, 411.1622009277344, 547.1590576171875, 420.37518310546875], "spans": [[16, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 16, "row-header": false, "row-span": [16, 17]}], [{"bbox": [151.19720458984375, 398.68231201171875, 536.0410766601562, 407.8952941894531], "spans": [[17, 0]], "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 17, "row-header": false, "row-span": [17, 18]}, {"bbox": [541.595947265625, 398.68231201171875, 547.1508178710938, 407.8952941894531], "spans": [[17, 1]], "text": "8", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 17, "row-header": false, "row-span": [17, 18]}], [{"bbox": [151.19720458984375, 386.1426696777344, 536.0748901367188, 395.35565185546875], "spans": [[18, 0]], "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 18, "row-header": false, "row-span": [18, 19]}, {"bbox": [541.6302490234375, 386.1426696777344, 547.1856079101562, 395.35565185546875], "spans": [[18, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 18, "row-header": false, "row-span": [18, 19]}], [{"bbox": [151.19720458984375, 373.66278076171875, 411.2704772949219, 382.8757629394531], "spans": [[19, 0]], "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 19, "row-header": false, "row-span": [19, 20]}, {"bbox": [416.8177490234375, 373.66278076171875, 547.1786499023438, 382.8757629394531], "spans": [[19, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 19, "row-header": false, "row-span": [19, 20]}], [{"bbox": [151.19720458984375, 361.1828918457031, 536.035888671875, 370.3958740234375], "spans": [[20, 0]], "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 20, "row-header": false, "row-span": [20, 21]}, {"bbox": [541.5989379882812, 361.1828918457031, 547.1619262695312, 370.3958740234375], "spans": [[20, 1]], "text": "9", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 20, "row-header": false, "row-span": [20, 21]}], [{"bbox": [151.19720458984375, 348.64324951171875, 530.5731811523438, 357.8562316894531], "spans": [[21, 0]], "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 21, "row-header": false, "row-span": [21, 22]}, {"bbox": [536.1044311523438, 348.64324951171875, 547.1668701171875, 357.8562316894531], "spans": [[21, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 21, "row-header": false, "row-span": [21, 22]}], [{"bbox": [151.19720458984375, 336.1633605957031, 530.5352172851562, 345.3763427734375], "spans": [[22, 0]], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 22, "row-header": false, "row-span": [22, 23]}, {"bbox": [536.0755004882812, 336.1633605957031, 547.156005859375, 345.3763427734375], "spans": [[22, 1]], "text": "10", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 22, "row-header": false, "row-span": [22, 23]}], [{"bbox": [136.7970428466797, 323.6834716796875, 547.256591796875, 332.8964538574219], "spans": [[23, 0]], "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 23, "row-header": false, "row-span": [23, 24]}, {"bbox": null, "spans": [[23, 1]], "text": "", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 23, "row-header": false, "row-span": [23, 24]}], [{"bbox": [136.79702758789062, 301.183837890625, 530.5396118164062, 310.3968200683594], "spans": [[24, 0]], "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 24, "row-header": false, "row-span": [24, 25]}, {"bbox": [536.0916748046875, 301.183837890625, 547.19580078125, 310.3968200683594], "spans": [[24, 1]], "text": "13", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 24, "row-header": false, "row-span": [24, 25]}], [{"bbox": [136.79702758789062, 289.18402099609375, 530.4808959960938, 298.3970031738281], "spans": [[25, 0]], "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 25, "row-header": false, "row-span": [25, 26]}, {"bbox": [536.04248046875, 289.18402099609375, 547.1657104492188, 298.3970031738281], "spans": [[25, 1]], "text": "14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 25, "row-header": false, "row-span": [25, 26]}], [{"bbox": [151.1971893310547, 276.6443786621094, 378.2078552246094, 285.85736083984375], "spans": [[26, 0]], "text": "3.1.1 Row permission and column mask definitions", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 26, "row-header": false, "row-span": [26, 27]}, {"bbox": [383.74713134765625, 276.6443786621094, 547.15576171875, 285.85736083984375], "spans": [[26, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 26, "row-header": false, "row-span": [26, 27]}], [{"bbox": [151.1971893310547, 264.16448974609375, 530.4347534179688, 273.3774719238281], "spans": [[27, 0]], "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 27, "row-header": false, "row-span": [27, 28]}, {"bbox": [535.9962158203125, 264.16448974609375, 547.1190795898438, 273.3774719238281], "spans": [[27, 1]], "text": "16", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 27, "row-header": false, "row-span": [27, 28]}], [{"bbox": [136.79702758789062, 251.6248321533203, 530.528076171875, 260.83782958984375], "spans": [[28, 0]], "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 28, "row-header": false, "row-span": [28, 29]}, {"bbox": [536.0670166015625, 251.6248321533203, 547.1448364257812, 260.83782958984375], "spans": [[28, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 28, "row-header": false, "row-span": [28, 29]}], [{"bbox": [151.1971893310547, 239.14495849609375, 530.4978637695312, 248.3579559326172], "spans": [[29, 0]], "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 29, "row-header": false, "row-span": [29, 30]}, {"bbox": [536.0518798828125, 239.14495849609375, 547.159912109375, 248.3579559326172], "spans": [[29, 1]], "text": "18", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 29, "row-header": false, "row-span": [29, 30]}], [{"bbox": [151.1971893310547, 226.6650848388672, 530.5602416992188, 235.87808227539062], "spans": [[30, 0]], "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 30, "row-header": false, "row-span": [30, 31]}, {"bbox": [536.09912109375, 226.6650848388672, 547.1768798828125, 235.87808227539062], "spans": [[30, 1]], "text": "19", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 30, "row-header": false, "row-span": [30, 31]}], [{"bbox": [136.79702758789062, 214.1254425048828, 530.5302734375, 223.33843994140625], "spans": [[31, 0]], "text": "3.3 VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 31, "row-header": false, "row-span": [31, 32]}, {"bbox": [536.0615234375, 214.1254425048828, 547.1240234375, 223.33843994140625], "spans": [[31, 1]], "text": "20", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 31, "row-header": false, "row-span": [31, 32]}], [{"bbox": [136.79702758789062, 201.64556884765625, 530.6299438476562, 210.8585662841797], "spans": [[32, 0]], "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 32, "row-header": false, "row-span": [32, 33]}, {"bbox": [536.1631469726562, 201.64556884765625, 547.2295532226562, 210.8585662841797], "spans": [[32, 1]], "text": "21", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 32, "row-header": false, "row-span": [32, 33]}], [{"bbox": [400.3206481933594, 189.1656951904297, 530.4835815429688, 198.37869262695312], "spans": [[33, 0]], "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 33, "row-header": false, "row-span": [33, 34]}, {"bbox": [536.0223999023438, 189.1656951904297, 547.10009765625, 198.37869262695312], "spans": [[33, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 33, "row-header": false, "row-span": [33, 34]}], [{"bbox": [136.79701232910156, 176.6260528564453, 530.5651245117188, 198.37869262695312], "spans": [[34, 0]], "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 34, "row-header": false, "row-span": [34, 35]}, {"bbox": [536.1119995117188, 176.6260528564453, 547.2057495117188, 185.83905029296875], "spans": [[34, 1]], "text": "22", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 34, "row-header": false, "row-span": [34, 35]}], [{"bbox": [151.19717407226562, 164.14617919921875, 530.4913940429688, 173.3591766357422], "spans": [[35, 0]], "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 35, "row-header": false, "row-span": [35, 36]}, {"bbox": [536.0463256835938, 164.14617919921875, 547.1561889648438, 173.3591766357422], "spans": [[35, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 35, "row-header": false, "row-span": [35, 36]}], [{"bbox": [151.19717407226562, 151.6663055419922, 530.5645751953125, 160.87930297851562], "spans": [[36, 0]], "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 36, "row-header": false, "row-span": [36, 37]}, {"bbox": [536.0960083007812, 151.6663055419922, 547.1587524414062, 160.87930297851562], "spans": [[36, 1]], "text": "23", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 36, "row-header": false, "row-span": [36, 37]}], [{"bbox": [151.19717407226562, 139.1266632080078, 530.5569458007812, 148.33966064453125], "spans": [[37, 0]], "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 37, "row-header": false, "row-span": [37, 38]}, {"bbox": [536.0881958007812, 139.1266632080078, 547.1507568359375, 148.33966064453125], "spans": [[37, 1]], "text": "24", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 37, "row-header": false, "row-span": [37, 38]}], [{"bbox": [151.19717407226562, 126.64678955078125, 530.5341186523438, 135.8597869873047], "spans": [[38, 0]], "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 38, "row-header": false, "row-span": [38, 39]}, {"bbox": [536.072998046875, 126.64678955078125, 547.15087890625, 135.8597869873047], "spans": [[38, 1]], "text": "25", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 38, "row-header": false, "row-span": [38, 39]}], [{"bbox": [151.19717407226562, 114.16690826416016, 339.4510498046875, 123.37991333007812], "spans": [[39, 0]], "text": "3.6.5 Defining and creating column masks", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 39, "row-header": false, "row-span": [39, 40]}, {"bbox": [344.9899597167969, 114.16690826416016, 547.160888671875, 123.37991333007812], "spans": [[39, 1]], "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 39, "row-header": false, "row-span": [39, 40]}], [{"bbox": [151.19717407226562, 101.62727355957031, 530.541015625, 110.84027099609375], "spans": [[40, 0]], "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 40, "row-header": false, "row-span": [40, 41]}, {"bbox": [536.087646484375, 101.62727355957031, 547.1808471679688, 110.84027099609375], "spans": [[40, 1]], "text": "28", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 40, "row-header": false, "row-span": [40, 41]}], [{"bbox": [151.19717407226562, 89.14738464355469, 530.5750732421875, 98.36038970947266], "spans": [[41, 0]], "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 41, "row-header": false, "row-span": [41, 42]}, {"bbox": [536.1066284179688, 89.14738464355469, 547.169677734375, 98.36038970947266], "spans": [[41, 1]], "text": "29", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 41, "row-header": false, "row-span": [41, 42]}], [{"bbox": [151.19717407226562, 76.6675033569336, 530.436279296875, 85.88050842285156], "spans": [[42, 0]], "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 42, "row-header": false, "row-span": [42, 43]}, {"bbox": [535.9984741210938, 76.6675033569336, 547.1228637695312, 85.88050842285156], "spans": [[42, 1]], "text": "32", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 42, "row-header": false, "row-span": [42, 43]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [135.52462768554688, 349.949462890625, 545.8714599609375, 502.2747802734375], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 3, "#-rows": 5, "data": [[{"bbox": [142.8000030517578, 487.1369934082031, 202.2449951171875, 495.4620056152344], "spans": [[0, 0]], "text": "Column name", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [216.8087921142578, 487.1369934082031, 257.210693359375, 495.4620056152344], "spans": [[0, 1]], "text": "Data type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [289.47479248046875, 487.1369934082031, 338.8946838378906, 495.4620056152344], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8000030517578, 468.1172790527344, 203.2322998046875, 476.4422912597656], "spans": [[1, 0]], "text": "FUNCTION_ID", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [216.785400390625, 468.1172790527344, 276.00360107421875, 476.4422912597656], "spans": [[1, 1]], "text": "VARCHAR(30)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [289.45770263671875, 468.1172790527344, 359.85394287109375, 476.4422912597656], "spans": [[1, 2]], "text": "ID of the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8000030517578, 449.156982421875, 198.66929626464844, 457.48199462890625], "spans": [[2, 0]], "text": "USER_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [216.74130249023438, 449.156982421875, 275.9234924316406, 457.48199462890625], "spans": [[2, 1]], "text": "VARCHAR(10)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [289.382080078125, 438.1166687011719, 515.0535888671875, 457.48199462890625], "spans": [[2, 2]], "text": "Name of the user profile that has a usage setting for this  function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.79998779296875, 419.1563720703125, 173.98318481445312, 427.48138427734375], "spans": [[3, 0]], "text": "USAGE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [216.773681640625, 419.1563720703125, 270.9797668457031, 427.48138427734375], "spans": [[3, 1]], "text": "VARCHAR(7)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [289.416259765625, 397.13604736328125, 539.1071166992188, 427.48138427734375], "spans": [[3, 2]], "text": "Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [142.8000030517578, 378.1163330078125, 196.2248992919922, 386.44134521484375], "spans": [[4, 0]], "text": "USER_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [216.75210571289062, 378.1163330078125, 270.99871826171875, 386.44134521484375], "spans": [[4, 1]], "text": "VARCHAR(5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [289.4316101074219, 356.15631103515625, 448.11962890625, 386.44134521484375], "spans": [[4, 2]], "text": "Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [64.41139221191406, 70.39208984375, 547.3950805664062, 398.3863830566406], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 6, "#-rows": 13, "data": [[{"bbox": [70.80030059814453, 383.1567077636719, 119.78550720214844, 391.4817199707031], "spans": [[0, 0]], "text": "User action", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": true, "row-span": [0, 1]}, {"bbox": [424.93804931640625, 304.9800109863281, 433.2629699707031, 344.4774475097656], "spans": [[0, 1]], "text": "*JOBCTL", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [450.1380615234375, 304.9800109863281, 458.4629821777344, 390.3999328613281], "spans": [[0, 2]], "text": "QIBM_DB_SECADM", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [475.9383544921875, 304.9800109863281, 484.2632751464844, 390.465576171875], "spans": [[0, 3]], "text": "QIBM_DB_SQLADM", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [501.13836669921875, 304.9800109863281, 509.4632873535156, 390.385498046875], "spans": [[0, 4]], "text": "QIBM_DB_SYSMON", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [526.3986206054688, 304.9799499511719, 534.7235717773438, 359.2005615234375], "spans": [[0, 5]], "text": "No Authority", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 285.11700439453125, 220.1568145751953, 293.4420166015625], "spans": [[1, 0]], "text": "SET CURRENT DEGREE  (SQL statement)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}, {"bbox": [429.0, 285.11700439453125, 435.00299072265625, 293.4420166015625], "spans": [[1, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [480.00030517578125, 285.11700439453125, 486.0032958984375, 293.4420166015625], "spans": [[1, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 266.1567077636719, 264.5538024902344, 274.4817199707031], "spans": [[2, 0]], "text": "CHGQRYA  command targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [429.0000305175781, 266.1567077636719, 435.0030212402344, 274.4817199707031], "spans": [[2, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [480.0003356933594, 266.1567077636719, 486.0033264160156, 274.4817199707031], "spans": [[2, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.800048828125, 247.1370086669922, 322.5057373046875, 255.46202087402344], "spans": [[3, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [429.0000305175781, 247.1370086669922, 435.0030212402344, 255.46202087402344], "spans": [[3, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [480.0003356933594, 247.1370086669922, 486.0033264160156, 255.46202087402344], "spans": [[3, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.800048828125, 228.1173095703125, 381.0218505859375, 236.44232177734375], "spans": [[4, 0]], "text": "STRDBMON  or  ENDDBMON  commands targeting a job that matches the current user", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [429.0000305175781, 228.1173095703125, 435.0030212402344, 236.44232177734375], "spans": [[4, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [480.0003356933594, 228.1173095703125, 486.0033264160156, 236.44232177734375], "spans": [[4, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [505.2606201171875, 228.1173095703125, 511.26361083984375, 236.44232177734375], "spans": [[4, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [530.7603149414062, 228.1173095703125, 536.7633056640625, 236.44232177734375], "spans": [[4, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.800048828125, 209.15701293945312, 359.5173645019531, 217.48202514648438], "spans": [[5, 0]], "text": "QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [429.00006103515625, 209.15701293945312, 435.0030517578125, 217.48202514648438], "spans": [[5, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [480.0003662109375, 209.15701293945312, 486.00335693359375, 217.48202514648438], "spans": [[5, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [505.26068115234375, 209.15701293945312, 511.263671875, 217.48202514648438], "spans": [[5, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.80007934570312, 190.13731384277344, 220.7517852783203, 198.4623260498047], "spans": [[6, 0]], "text": "Visual Explain within Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [429.00006103515625, 190.13731384277344, 435.0030517578125, 198.4623260498047], "spans": [[6, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": null, "spans": [[6, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [480.0003662109375, 190.13731384277344, 486.00335693359375, 198.4623260498047], "spans": [[6, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [505.26068115234375, 190.13731384277344, 511.263671875, 198.4623260498047], "spans": [[6, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [530.7603759765625, 190.13731384277344, 536.7633666992188, 198.4623260498047], "spans": [[6, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.80007934570312, 171.11761474609375, 236.65480041503906, 179.442626953125], "spans": [[7, 0]], "text": "Visual Explain outside of Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}, {"bbox": [429.00006103515625, 171.11761474609375, 435.0030517578125, 179.442626953125], "spans": [[7, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [480.0003662109375, 171.11761474609375, 486.00335693359375, 179.442626953125], "spans": [[7, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.80007934570312, 152.15731811523438, 213.1296844482422, 160.48233032226562], "spans": [[8, 0]], "text": "ANALYZE PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}, {"bbox": [429.00006103515625, 152.15731811523438, 435.0030517578125, 160.48233032226562], "spans": [[8, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [480.0003662109375, 152.15731811523438, 486.00335693359375, 160.48233032226562], "spans": [[8, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [70.80007934570312, 133.1376190185547, 199.87808227539062, 141.46263122558594], "spans": [[9, 0]], "text": "DUMP PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [429.00006103515625, 133.1376190185547, 435.0030517578125, 141.46263122558594], "spans": [[9, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [480.0003662109375, 133.1376190185547, 486.00335693359375, 141.46263122558594], "spans": [[9, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 9, "row-header": false, "row-span": [9, 10]}], [{"bbox": [70.80007934570312, 114.11792755126953, 208.36776733398438, 122.44291687011719], "spans": [[10, 0]], "text": "MODIFY PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}, {"bbox": [429.00006103515625, 114.11792755126953, 435.0030517578125, 122.44291687011719], "spans": [[10, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": [480.0003662109375, 114.11792755126953, 486.00335693359375, 122.44291687011719], "spans": [[10, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 10, "row-header": false, "row-span": [10, 11]}], [{"bbox": [70.80007934570312, 95.09822845458984, 411.20263671875, 103.42323303222656], "spans": [[11, 0]], "text": "MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}, {"bbox": [429.00006103515625, 95.09822845458984, 435.0030517578125, 103.42323303222656], "spans": [[11, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": [480.0003662109375, 95.09822845458984, 486.00335693359375, 103.42323303222656], "spans": [[11, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 11, "row-header": false, "row-span": [11, 12]}], [{"bbox": [70.80007934570312, 76.13793182373047, 377.1258544921875, 84.46292877197266], "spans": [[12, 0]], "text": "CHANGE PLAN CACHE SIZE procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}, {"bbox": [429.00006103515625, 76.13793182373047, 435.0030517578125, 84.46292877197266], "spans": [[12, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 2]], "text": "", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": [480.0003662109375, 76.13793182373047, 486.00335693359375, 84.46292877197266], "spans": [[12, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 4]], "text": "", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 5]], "text": "", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 12, "row-header": false, "row-span": [12, 13]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [134.5463104248047, 587.7283935546875, 542.0460205078125, 688.5811157226562], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "table", "payload": null, "#-cols": 2, "#-rows": 4, "data": [[{"bbox": [142.8000030517578, 673.1370239257812, 209.67091369628906, 681.4619750976562], "spans": [[0, 0]], "text": "Special register", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [230.18911743164062, 673.1370239257812, 319.9352722167969, 681.4619750976562], "spans": [[0, 1]], "text": "Corresponding value", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.80001831054688, 643.1364135742188, 212.7012176513672, 662.5016479492188], "spans": [[1, 0]], "text": "USER or SESSION_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [230.2197265625, 654.1766967773438, 467.9906921386719, 662.5016479492188], "spans": [[1, 1]], "text": "The effective user of the thread excluding adopted authority.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.80003356933594, 624.11669921875, 216.63963317871094, 632.441650390625], "spans": [[2, 0]], "text": "CURRENT_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [230.19813537597656, 613.13671875, 535.6508178710938, 632.441650390625], "spans": [[2, 1]], "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.8009033203125, 594.1170043945312, 209.73570251464844, 602.4419555664062], "spans": [[3, 0]], "text": "SYSTEM_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [230.24490356445312, 594.1170043945312, 425.64569091796875, 602.4419555664062], "spans": [[3, 1]], "text": "The authorization ID that initiated the connection.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [63.55636978149414, 495.77532958984375, 548.5687255859375, 687.76611328125], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "table", "payload": null, "#-cols": 3, "#-rows": 10, "data": [[{"bbox": [70.80000305175781, 673.1370239257812, 134.99070739746094, 681.4619750976562], "spans": [[0, 0]], "text": "Global variable", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [202.889404296875, 673.1370239257812, 223.34640502929688, 681.4619750976562], "spans": [[0, 1]], "text": "Type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [281.8247985839844, 673.1370239257812, 331.3428039550781, 681.4619750976562], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.80000305175781, 654.1766967773438, 132.7209014892578, 662.5016479492188], "spans": [[1, 0]], "text": "CLIENT_HOST", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [202.89028930664062, 654.1766967773438, 267.0765075683594, 662.5016479492188], "spans": [[1, 1]], "text": "VARCHAR(255)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [281.8473205566406, 654.1766967773438, 510.17547607421875, 662.5016479492188], "spans": [[1, 2]], "text": "Host name of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.80001831054688, 635.156982421875, 140.66522216796875, 643.48193359375], "spans": [[2, 0]], "text": "CLIENT_IPADDR", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [202.872314453125, 635.156982421875, 267.077392578125, 643.48193359375], "spans": [[2, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, 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used by the current client to communicate with the server", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.80001831054688, 597.1175537109375, 143.50924682617188, 605.4425048828125], "spans": [[4, 0]], "text": "PACKAGE_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [202.80575561523438, 597.1175537109375, 267.0693664550781, 605.4425048828125], "spans": [[4, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [281.85186767578125, 597.1175537109375, 436.5726013183594, 605.4425048828125], "spans": [[4, 2]], "text": "Name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.80001831054688, 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diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
index 790bda46..65921be8 100644
--- a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
+++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json
@@ -1 +1 @@
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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", "bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "3.6.4", "bbox": {"l": 151.19717, "t": 656.14021, "r": 173.35289, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89182, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "25", "bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "3.6.5", "bbox": {"l": 151.19717, "t": 668.62009, "r": 173.35289, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Defining and creating column masks", "bbox": {"l": 178.89182, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.98996, "t": 668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "29", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "32", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. 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This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.987220287322998, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. 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A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.982377827167511, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.5288543701172, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41448974609375, "t": 527.24462890625, "r": 252.0884246826172, "b": 635.3829956054688, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174706935882568, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.987220287322998, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.982377827167511, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.5288543701172, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41448974609375, "t": 527.24462890625, "r": 252.0884246826172, "b": 635.3829956054688, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040938019752502, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734501242637634, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8513590097427368, "cells": [{"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578531742095947, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578531742095947, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": 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612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166074395179749, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551493525505066, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808837413787842, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 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"cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": 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"TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631589293479919, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": 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0.5340578556060791, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171271562576294, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373097658157349, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167067050933838, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656645059585571, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.985310435295105, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52508544921875, "t": 289.7249755859375, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 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0.9551493525505066, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808837413787842, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631589293479919, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}, "confidence": 0.5345606803894043, "cells": [{"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5818131566047668, "cells": [{"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5035313963890076, "cells": [{"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5340578556060791, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171271562576294, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373097658157349, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167067050933838, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656645059585571, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.985310435295105, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52508544921875, "t": 289.7249755859375, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 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"USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 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GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166074395179749, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987305164337158, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789127707481384, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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Roles and separation of duties"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987305164337158, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789127707481384, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557499885559082, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130505323410034, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164151191711, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.946666419506073, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 16, "label": "picture", "bbox": {"l": 135.64842224121094, "t": 384.17364501953125, "r": 301.23675537109375, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221464514732361, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": 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"used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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"TOPLEFT"}, "confidence": 0.7875164151191711, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.946666419506073, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64842224121094, "t": 384.17364501953125, 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"confidence": 0.9557499885559082, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130505323410034, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": 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342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370970487594604, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 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281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370970487594604, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187918305397034, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505078196525574, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056529998779297, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892627477645874, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509141445159912, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.1495819091797, "t": 132.0330352783203, "r": 547.52685546875, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803680181503296, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 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"TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473597407341003, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892627477645874, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509141445159912, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1495819091797, "t": 132.0330352783203, "r": 547.52685546875, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803680181503296, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "29", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "32", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509141445159912, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1495819091797, "t": 132.0330352783203, "r": 547.52685546875, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803680181503296, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174709916114807, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255505204200745, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318943977356, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41445922851562, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040942788124084, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865840077400208, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077319264411926, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837489128112793, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621999263763428, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889443874359131, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.949161946773529, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858148097991943, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.9757603406906128, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838991165161133, "cells": 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612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551584124565125, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092966079712, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196997284889221, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647753834724426, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159966468811035, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808269381523132, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 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"cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092966079712, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196997284889221, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647753834724426, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159966468811035, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": 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"TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631120204925537, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": 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0.5340272188186646, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171727538108826, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.537347137928009, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167021751403809, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656103849411011, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 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0.9551584124565125, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092966079712, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196997284889221, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647753834724426, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159966468811035, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808269381523132, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631120204925537, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}, "confidence": 0.5346829891204834, "cells": [{"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5818389058113098, "cells": [{"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5035732388496399, "cells": [{"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5340272188186646, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171727538108826, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.537347137928009, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167021751403809, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656103849411011, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 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"USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 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GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987306356430054, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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Roles and separation of duties"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987306356430054, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665539860725403, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221462726593018, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": 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"used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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"TOPLEFT"}, "confidence": 0.7875164747238159, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445975422859192, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748277068138123, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274529814720154, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5463104248047, "t": 103.41889190673828, "r": 542.0460205078125, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1736755371094, 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"confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130509495735168, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": 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342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 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281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370980620384216, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132355451583862, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237534403800964, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338628649711609, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514462947845459, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512302875518799, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.706649661064148, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187921285629272, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505080580711365, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606333613395691, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897271037101746, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785220146179199, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678944706916809, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467734694480896, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705167412757874, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469641447067261, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003496885299683, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.848820149898529, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411225080490112, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.910578727722168, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570101976394653, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.600240170955658, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985856056213379, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449256062507629, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80192184448242, "t": 95.38238525390625, "r": 547.11474609375, "b": 170.0321044921875, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.9049987792969, "r": 530.0478515625, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801458716392517, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632163763046265, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994000554084778, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497195363044739, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 17, "cluster": {"id": 0, "label": "text", "bbox": {"l": 152.94002, "t": 331.70728, "r": 414.17383, "b": 388.57095, "coord_origin": "TOPLEFT"}, "confidence": 0.9799742698669434, "cells": [{"id": 44, "text": "This paper is intended for database engineers, data-centric application ", "bbox": {"l": 152.94002, "t": 331.70728, "r": 414.17383, "b": 340.57169, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "developers, and security officers who want to design and implement ", "bbox": {"l": 152.94002, "t": 343.70709, "r": 407.61029, "b": 352.5715, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "RCAC as a part of their data control and governance policy. 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diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
index edc5c84b..eaee8448 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt
@@ -106,12 +106,12 @@
 <text><location><page_6><loc_8><loc_70><loc_47><loc_80></location>The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.</text>
 <text><location><page_6><loc_8><loc_44><loc_47><loc_69></location>Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.</text>
 <text><location><page_6><loc_8><loc_41><loc_47><loc_43></location>The loss used to train the TableFormer can be defined as following:</text>
-<formula><location><page_6><loc_20><loc_35><loc_47><loc_38></location>l$_{box}$ = λ$_{iou}$l$_{iou}$ + λ$_{l}$$_{1}$ l = λl$_{s}$ + (1 - λ ) l$_{box}$ (1)</formula>
+<formula><location><page_6><loc_20><loc_35><loc_47><loc_38></location></formula>
 <text><location><page_6><loc_8><loc_32><loc_46><loc_33></location>where λ ∈ [0, 1], and λ$_{iou}$, λ$_{l}$$_{1}$ ∈$_{R}$ are hyper-parameters.</text>
 <section_header_level_1><location><page_6><loc_8><loc_28><loc_28><loc_30></location>5. Experimental Results</section_header_level_1>
 <section_header_level_1><location><page_6><loc_8><loc_26><loc_29><loc_27></location>5.1. Implementation Details</section_header_level_1>
 <text><location><page_6><loc_8><loc_19><loc_47><loc_25></location>TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:</text>
-<formula><location><page_6><loc_15><loc_14><loc_47><loc_17></location>Image width and height ≤ 1024 pixels Structural tags length ≤ 512 tokens. (2)</formula>
+<formula><location><page_6><loc_15><loc_14><loc_47><loc_17></location></formula>
 <text><location><page_6><loc_8><loc_10><loc_47><loc_13></location>Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved</text>
 <text><location><page_6><loc_50><loc_86><loc_89><loc_91></location>runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.</text>
 <text><location><page_6><loc_50><loc_59><loc_89><loc_85></location>The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.</text>
@@ -122,7 +122,7 @@
 <text><location><page_6><loc_50><loc_10><loc_89><loc_14></location>We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.</text>
 <section_header_level_1><location><page_7><loc_8><loc_89><loc_27><loc_91></location>5.3. Datasets and Metrics</section_header_level_1>
 <text><location><page_7><loc_8><loc_83><loc_47><loc_88></location>The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:</text>
-<formula><location><page_7><loc_14><loc_78><loc_47><loc_81></location>TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)</formula>
+<formula><location><page_7><loc_14><loc_78><loc_47><loc_81></location></formula>
 <text><location><page_7><loc_8><loc_73><loc_47><loc_77></location>where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .</text>
 <section_header_level_1><location><page_7><loc_8><loc_70><loc_28><loc_72></location>5.4. Quantitative Analysis</section_header_level_1>
 <text><location><page_7><loc_8><loc_50><loc_47><loc_69></location>Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.</text>
@@ -304,7 +304,7 @@
 <list_item><location><page_12><loc_8><loc_29><loc_47><loc_33></location>3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.</list_item>
 <list_item><location><page_12><loc_8><loc_24><loc_47><loc_28></location>4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:</list_item>
 </unordered_list>
-<formula><location><page_12><loc_18><loc_17><loc_47><loc_21></location>alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)</formula>
+<formula><location><page_12><loc_18><loc_17><loc_47><loc_21></location></formula>
 <text><location><page_12><loc_8><loc_13><loc_47><loc_16></location>where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.</text>
 <unordered_list>
 <list_item><location><page_12><loc_8><loc_10><loc_47><loc_13></location>5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-</list_item>
diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.json b/tests/data/groundtruth/docling_v2/2203.01017v2.json
index 70d35fd8..7fc63165 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.json
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.json
@@ -1 +1 @@
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ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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"orig": "17", "text": "17"}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 394.10422, "t": 306.87531, "r": 402.88831, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "18", "text": "18"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 440.95941000000005, "t": 306.87531, "r": 449.7435, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 487.81491, "t": 306.87531, "r": 496.599, "b": 298.09036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 366.70102, "t": 342.87918, "r": 372.70581, "b": 332.74878, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.90424, "t": 318.67709, "r": 337.90903, "b": 308.54669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 459.87621999999993, "t": 354.4064, "r": 465.88101, "b": 344.276, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 277.4996337890625, "r": 545.1151733398438, "b": 232.7270965576172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 220]}], "orig": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.79849, "t": 712.1882300000001, "r": 486.84909, "b": 703.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "level": 1}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 396.76776, "t": 549.97302, "r": 469.78748, "b": 541.22504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.97653, "t": 558.57703, "r": 324.79254, "b": 552.745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 410.483, "t": 558.57703, "r": 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"parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.7735, "t": 661.21899, "r": 542.73877, "b": 653.92902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8K", "text": "8K"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.79901, "t": 638.07648, "r": 542.76428, "b": 630.7865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6K", "text": "6K"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.5705, "t": 615.242, "r": 542.53577, "b": 607.95203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4K", "text": "4K"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.14551, "t": 592.3537, "r": 542.11078, "b": 585.06372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2K", "text": "2K"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. 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The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. 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"bbox": {"l": 54.235424, "t": 418.18768, "r": 59.30449699999999, "b": 413.54578000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x2", "text": "x2"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 85.295891, "t": 307.46811, "r": 122.16431, "b": 301.63312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 229.66599, "t": 512.45392, "r": 265.3194, "b": 506.54427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 157.17369, "t": 291.6969, "r": 190.41711, "b": 285.87057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Predicted Tags", "text": "Predicted Tags"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 227.81598999999997, "t": 353.94458, "r": 270.78442, "b": 348.10794, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Bounding Boxes &", "text": "Bounding Boxes &"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 233.70262, "t": 347.93817, "r": 263.51105, "b": 342.1095000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Classification", "text": "Classification"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 184.74655, "t": 498.60498, "r": 212.16055, "b": 493.24097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Transformer", "text": "Transformer"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 178.91229, "t": 492.85498, "r": 216.74378999999996, "b": 487.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Decoder Network", "text": "Decoder Network"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 194.24574, "t": 509.2178, "r": 198.89099, "b": 504.15182000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x4", "text": "x4"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 221.45587, "t": 520.13086, "r": 276.47089, "b": 514.17084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CELL BBOX DECODER", "text": "CELL BBOX DECODER"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 151.65219, "t": 468.55759, "r": 197.29019, "b": 462.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Masked Multi-Head", "text": "Masked Multi-Head"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 163.43277, "t": 462.55759, "r": 184.19028, "b": 456.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Attention", "text": "Attention"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 437.37939, "t": 391.44705, "r": 443.69870000000003, "b": 385.12842, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 450.33203, "t": 391.44705, "r": 456.6513100000001, "b": 385.12842, "coord_origin": 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[0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/345", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 424.42719, "t": 385.22536999999994, "r": 430.74648999999994, "b": 378.90674, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/346", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 502.86941999999993, "t": 381.00562, "r": 509.18871999999993, "b": 374.68698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "29", "text": "29"}, {"self_ref": "#/texts/347", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 384.35437, "t": 410.22278, "r": 430.99261, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Predicted Structure", "text": "Predicted Structure"}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595001220703125, "t": 333.2716369628906, "r": 532.6304931640625, "b": 324.3650817871094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"self_ref": "#/texts/349", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 119.90107727050781, "r": 364.4058532714844, "b": 109.15335845947266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 313.3450012207031, "t": 98.0382080078125, "r": 545.1134033203125, "b": 79.06324768066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. 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"label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)"}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 302.59356689453125, "b": 48.9600944519043, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/514", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.8619689941406, "t": 139.0646514892578, "r": 545.1151123046875, "b": 118.20308685302734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact."}, {"self_ref": "#/texts/515", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.11199951171875, "t": 283.6626281738281, "r": 545.1138305664062, "b": 262.80108642578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"self_ref": "#/texts/516", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 315.6535949707031, "t": 563.2764892578125, "r": 537.1474609375, "b": 489.1985168457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/13"}, {"cref": "#/texts/14"}, 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"label": "list"}, {"self_ref": "#/groups/14", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/490"}, {"cref": "#/texts/491"}, {"cref": "#/texts/492"}, {"cref": "#/texts/493"}], "name": "list", "label": "list"}, {"self_ref": "#/groups/15", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/495"}, {"cref": "#/texts/496"}, {"cref": "#/texts/497"}, {"cref": "#/texts/498"}], "name": "list", "label": "list"}], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 18.340221405029297, "t": 584.1799926757812, "r": 36.339778900146484, "b": 231.99996948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 96.3010025024414, "t": 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ahn,nli,mly,taa } @zurich.ibm.com", "text": "{ ahn,nli,mly,taa } @zurich.ibm.com"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.99497985839844, "t": 576.5170288085938, "r": 190.48028564453125, "b": 565.769287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 573.9931640625, "r": 408.4407043457031, "b": 565.2451782226562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 252.05723571777344, "r": 126.94803619384766, "b": 241.30950927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 231.216796875, "r": 286.3650817871094, "b": 78.84822082519531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.9457100000001, "t": 556.65295, "r": 457.95050000000003, "b": 546.52252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.19681, "t": 522.64734, "r": 337.2016, "b": 512.51691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.0329, "t": 539.32104, "r": 390.03769, "b": 529.19061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 50.111976623535156, "t": 550.6049194335938, "r": 286.3651123046875, "b": 279.00335693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.5670166015625, "t": 478.3052062988281, "r": 486.4019470214844, "b": 458.7572021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "text": "b. 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366.70102, "t": 342.87918, "r": 372.70581, "b": 332.74878, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.90424, "t": 318.67709, "r": 337.90903, "b": 308.54669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 459.87621999999993, "t": 354.4064, "r": 465.88101, "b": 344.276, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 277.4996337890625, "r": 545.1151733398438, "b": 232.7270965576172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 220]}], "orig": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 207.59063720703125, "r": 545.1151733398438, "b": 126.95307159423828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.86199951171875, "t": 123.61963653564453, "r": 545.1151123046875, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.36505126953125, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 692.4285888671875, "r": 286.3651428222656, "b": 563.9699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 560.4684448242188, "r": 286.3651123046875, "b": 420.054931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11199951171875, "t": 416.5534973144531, "r": 286.3665771484375, "b": 359.8269958496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 242]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 347.568115234375, "r": 286.3648986816406, "b": 302.6770324707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.56901550292969, "t": 289.9661560058594, "r": 286.3648986816406, "b": 245.0740509033203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569000244140625, "t": 232.3631591796875, "r": 286.36492919921875, "b": 199.4270477294922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569007873535156, "t": 186.5966033935547, "r": 286.3650817871094, "b": 153.779052734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.11200714111328, "t": 141.401611328125, "r": 286.3651123046875, "b": 96.63004302978516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.97100067138672, "t": 86.40372467041016, "r": 183.7305450439453, "b": 79.27845764160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "$^{1}$https://github.com/IBM/SynthTabNet", "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.1210021972656, "t": 57.86671829223633, "r": 300.102294921875, "b": 48.96015548706055, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151123046875, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 670.26806640625, "r": 498.28021240234375, "b": 659.5203247070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 649.7786254882812, "r": 545.1151733398438, "b": 461.54498291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.86199951171875, "t": 458.4305419921875, "r": 545.115234375, "b": 341.9270935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.8619689941406, "t": 338.9322204589844, "r": 545.1168823242188, "b": 78.84815216064453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \"image-encoder \u2192 text-decoder\" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \"image-encoder \u2192 dual decoder\" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 250.15101623535156, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199951171875, "t": 704.7806396484375, "r": 286.3651428222656, "b": 516.5458984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11199188232422, "t": 513.56103515625, "r": 286.3651123046875, "b": 301.297119140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 298.3112487792969, "r": 286.36627197265625, "b": 169.733154296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 619]}], "orig": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 156.05516052246094, "r": 105.22545623779297, "b": 145.30743408203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.11198425292969, "t": 135.57470703125, "r": 286.3650817871094, "b": 78.84813690185547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.1210021972656, "t": 57.86680221557617, "r": 300.102294921875, "b": 48.96023941040039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 524.1636352539062, "r": 545.1151123046875, "b": 503.3020935058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.79849, "t": 712.1882300000001, "r": 486.84909, "b": 703.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "level": 1}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 396.76776, "t": 549.97302, "r": 469.78748, "b": 541.22504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.97653, "t": 558.57703, "r": 324.79254, "b": 552.745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 410.483, "t": 558.57703, "r": 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2K", "text": "2K"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 474.5266418457031, "r": 437.27001953125, "b": 465.6200866699219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 460.4686279296875, "r": 545.1151733398438, "b": 164.6382598876953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \"simple\" when it does not contain row spans or column spans, otherwise it is \"complex\". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.86199951171875, "t": 159.48580932617188, "r": 545.1151123046875, "b": 78.84823608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651123046875, "b": 695.9300537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 691.0396118164062, "r": 286.3651428222656, "b": 478.8949279785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 474.0044860839844, "r": 286.3651123046875, "b": 357.50103759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11199951171875, "t": 352.610595703125, "r": 286.3665466308594, "b": 164.37611389160156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.11201477050781, "t": 159.4856719970703, "r": 286.3651123046875, "b": 78.84810638427734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.1209716796875, "t": 57.86674880981445, "r": 300.1022644042969, "b": 48.96018600463867, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 624.338623046875, "r": 545.1150512695312, "b": 567.6110229492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both \"Combined-Tabnet\" and \"CombinedTabnet\" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 542.3795776367188, "r": 545.1151733398438, "b": 497.6080322265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.8169860839844, "t": 494.22760009765625, "r": 542.7439575195312, "b": 485.321044921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 470.8160400390625, "r": 444.9360656738281, "b": 460.0683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 450.06060791015625, "r": 545.115234375, "b": 345.5131530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.86199951171875, "t": 334.30572509765625, "r": 420.16058349609375, "b": 324.45367431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 315.2347106933594, "r": 545.11572265625, "b": 127.00019073486328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.8619689941406, "t": 123.73930358886719, "r": 545.1151123046875, "b": 78.84818267822266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.11199188232422, "t": 588.0142211914062, "r": 545.1084594726562, "b": 567.0330810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. 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The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 74.253464, "t": 533.78528, "r": 101.75846, "b": 527.82526, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Input Image", "text": "Input Image"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 122.29972, "t": 533.65479, "r": 157.83972, "b": 527.69476, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Tokenised Tags", "text": "Tokenised Tags"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 78.549347, "t": 420.61420000000004, "r": 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{"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 256.29767, "t": 386.7967499999999, "r": 271.77792, "b": 381.13474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Linear", "text": "Linear"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 239.54543, "t": 409.78656, "r": 258.08942, "b": 404.12454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Sigmoid", "text": "Sigmoid"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 54.14704100000001, "t": 407.12817, "r": 59.51152, "b": 342.21674, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "Transformer Encoder Network", "text": "Transformer Encoder Network"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 54.235424, "t": 418.18768, "r": 59.30449699999999, "b": 413.54578000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x2", "text": "x2"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 85.295891, "t": 307.46811, "r": 122.16431, "b": 301.63312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 229.66599, "t": 512.45392, "r": 265.3194, "b": 506.54427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Encoded Output", "text": "Encoded Output"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 157.17369, "t": 291.6969, "r": 190.41711, "b": 285.87057, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Predicted Tags", "text": "Predicted Tags"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 227.81598999999997, "t": 353.94458, "r": 270.78442, "b": 348.10794, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Bounding Boxes &", "text": "Bounding Boxes &"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 233.70262, "t": 347.93817, "r": 263.51105, "b": 342.1095000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Classification", "text": "Classification"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 184.74655, "t": 498.60498, "r": 212.16055, "b": 493.24097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Transformer", "text": "Transformer"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 178.91229, "t": 492.85498, "r": 216.74378999999996, "b": 487.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Decoder Network", "text": "Decoder Network"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 194.24574, "t": 509.2178, "r": 198.89099, "b": 504.15182000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "x4", "text": "x4"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 221.45587, "t": 520.13086, "r": 276.47089, "b": 514.17084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CELL BBOX DECODER", "text": "CELL BBOX DECODER"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 151.65219, "t": 468.55759, "r": 197.29019, "b": 462.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Masked Multi-Head", "text": "Masked Multi-Head"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 163.43277, "t": 462.55759, "r": 184.19028, "b": 456.89557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Attention", "text": "Attention"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.86199951171875, "t": 542.465576171875, "r": 545.1150512695312, "b": 497.69305419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 494.6601867675781, "r": 545.1151123046875, "b": 378.0381774902344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \"Scene Understanding\", \"Image Captioning\"), something which we relate to the simplicity of table images."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619689941406, "t": 374.8857421875, "r": 545.1151123046875, "b": 246.4272918701172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 243.39540100097656, "r": 545.1151123046875, "b": 138.727294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.8619384765625, "t": 135.57484436035156, "r": 545.1150512695312, "b": 78.84827423095703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.1209411621094, "t": 57.86684036254883, "r": 300.10223388671875, "b": 48.96027755737305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 286.3651428222656, "b": 636.1539916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 632.3755493164062, "r": 286.3651123046875, "b": 551.7369384765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11199951171875, "t": 548.0780639648438, "r": 286.36572265625, "b": 347.76910400390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112022399902344, "t": 343.9896545410156, "r": 286.364990234375, "b": 323.12811279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33001708984375, "t": 298.71905517578125, "r": 286.3624267578125, "b": 274.92828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 - \u03bb ) l$_{box}$ (1)", "text": ""}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112030029296875, "t": 261.4079895019531, "r": 281.596923828125, "b": 251.78411865234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 236.08311462402344, "r": 171.9833526611328, "b": 225.33538818359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 215.7356719970703, "r": 179.17501831054688, "b": 205.8836212158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/260", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.11204528808594, "t": 196.2656707763672, "r": 286.36517333984375, "b": 151.4931182861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.66104888916016, "t": 138.1719970703125, "r": 286.3624572753906, "b": 113.60411834716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": ""}, {"self_ref": "#/texts/262", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112060546875, "t": 99.70968627929688, "r": 286.3651428222656, "b": 78.8481216430664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.12103271484375, "t": 57.86667251586914, "r": 300.1023254394531, "b": 48.96010971069336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 716.7916870117188, "r": 545.115234375, "b": 683.97509765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"self_ref": "#/texts/265", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862060546875, "t": 675.7706298828125, "r": 545.1152954101562, "b": 463.6259460449219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two \"Transformer Encoder Layers\", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \"Transformer Decoder Layers\" with similar input and output dimensions as the \"Transformer Encoder Layers\". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"self_ref": "#/texts/266", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 455.4224853515625, "r": 545.1151733398438, "b": 362.83001708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"self_ref": "#/texts/267", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 354.6255798339844, "r": 545.115234375, "b": 238.12310791015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"self_ref": "#/texts/268", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 212.4456787109375, "r": 397.44281005859375, "b": 202.5936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/269", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 188.55067443847656, "r": 545.1151733398438, "b": 119.86811065673828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"self_ref": "#/texts/270", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.8620300292969, "t": 111.6646728515625, "r": 545.115234375, "b": 78.84710693359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"self_ref": "#/texts/271", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 717.5986328125, "r": 167.89825439453125, "b": 707.74658203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "level": 1}, {"self_ref": "#/texts/272", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 698.6495971679688, "r": 286.3651123046875, "b": 653.8770141601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.218994140625, "t": 641.6820068359375, "r": 286.3623962402344, "b": 619.26123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 - EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)", "text": ""}, {"self_ref": "#/texts/274", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11198425292969, "t": 610.9970092773438, "r": 286.36285400390625, "b": 578.02099609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 162]}], "orig": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 567.1805419921875, "r": 170.45169067382812, "b": 557.3284912109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "level": 1}, {"self_ref": "#/texts/276", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 548.35009765625, "r": 286.3651428222656, "b": 395.862060546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"self_ref": "#/texts/277", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 199.56663513183594, "r": 286.3651123046875, "b": 178.705078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11199951171875, "t": 175.65663146972656, "r": 261.7873229980469, "b": 166.7500762939453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned."}, {"self_ref": "#/texts/279", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.11201477050781, "t": 147.6501922607422, "r": 286.3659973144531, "b": 78.84806823730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.1210021972656, "t": 57.866641998291016, "r": 300.102294921875, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 716.7916259765625, "r": 545.1151733398438, "b": 564.4229125976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 737]}], "orig": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"self_ref": "#/texts/282", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 475.5506896972656, "r": 545.1151733398438, "b": 454.68914794921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"self_ref": "#/texts/283", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.8619689941406, "t": 424.3202819824219, "r": 545.1156616210938, "b": 271.8323059082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"self_ref": "#/texts/284", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.86199951171875, "t": 135.13864135742188, "r": 545.1151733398438, "b": 102.32206726074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"self_ref": "#/texts/285", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.28603744506836, "t": 713.3124389648438, "r": 61.550289154052734, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 65.68241882324219, "t": 713.3124389648438, "r": 499.5556335449219, "b": 705.4392700195312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 105]}], "orig": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 697.7188720703125, "r": 284.3459167480469, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "level": 1}, {"self_ref": "#/texts/288", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.830810546875, "t": 697.7188720703125, "r": 431.0911865234375, "b": 689.845703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "level": 1}, {"self_ref": "#/texts/289", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.81178283691406, "t": 583.7667236328125, "r": 385.93450927734375, "b": 575.8935546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.42730712890625, "t": 499.69573974609375, "r": 549.4217529296875, "b": 493.39715576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 471.1226501464844, "r": 545.11376953125, "b": 426.3501281738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset."}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715248, "t": 410.22278, "r": 85.657333, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 437.37939, "t": 391.44705, "r": 443.69870000000003, "b": 385.12842, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 450.33203, "t": 391.44705, "r": 456.6513100000001, "b": 385.12842, "coord_origin": 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"children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595001220703125, "t": 333.2716369628906, "r": 532.6304931640625, "b": 324.3650817871094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"self_ref": "#/texts/349", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.26282, "t": 410.22278, "r": 342.07819, "b": 405.55719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 300.6046447753906, "r": 163.75579833984375, "b": 290.7525939941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "level": 1}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.11199951171875, "t": 255.1266326904297, "r": 286.3651123046875, "b": 78.84805297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various \"complex\" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 301.29107666015625, "r": 460.8484802246094, "b": 290.5433654785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "level": 1}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 279.10662841796875, "r": 545.1151733398438, "b": 138.69407653808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.86199951171875, "t": 119.90107727050781, "r": 364.4058532714844, "b": 109.15335845947266, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 313.3450012207031, "t": 98.0382080078125, "r": 545.1134033203125, "b": 79.06324768066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/356", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 295.1210021972656, "t": 57.866634368896484, "r": 300.102294921875, "b": 48.9600715637207, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/357", "parent": {"cref": "#/groups/6"}, "children": [], "label": "list_item", "prov": [{"page_no": 9, "bbox": {"l": 70.03099822998047, "t": 716.1162109375, "r": 286.36334228515625, "b": 675.2242431640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "end object detection with transformers. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020 , pages 213-229, Cham, 2020. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.8419952392578, "t": 681.4251098632812, "r": 465.37591552734375, "b": 656.4699096679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "level": 1}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 630.839111328125, "r": 175.96437072753906, "b": 620.0913696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 611.0206909179688, "r": 150.364013671875, "b": 601.1686401367188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 592.0797119140625, "r": 286.3651428222656, "b": 403.8451843261719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 400.5947265625, "r": 286.3651123046875, "b": 164.54029846191406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 161.28985595703125, "r": 286.3649597167969, "b": 140.42730712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 129.60986328125, "r": 153.60784912109375, "b": 119.7578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.11198425292969, "t": 110.66886901855469, "r": 286.36505126953125, "b": 77.852294921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 629.3448486328125, "r": 545.1151123046875, "b": 584.572265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 580.7648315429688, "r": 545.1150512695312, "b": 559.9032592773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 556.0947875976562, "r": 545.1151123046875, "b": 475.45721435546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 471.6497802734375, "r": 545.1151733398438, "b": 343.19134521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 339.3839111328125, "r": 545.1151733398438, "b": 294.61138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 290.803955078125, "r": 545.1152954101562, "b": 246.0314178466797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 242.22396850585938, "r": 545.1151733398438, "b": 185.4964141845703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.86199951171875, "t": 169.70941162109375, "r": 545.1087646484375, "b": 145.01368713378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "level": 1}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.8620300292969, "t": 134.57896423339844, "r": 545.1151733398438, "b": 77.85139465332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.63104248046875, "t": 57.86696243286133, "r": 302.5936279296875, "b": 48.96039962768555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 626.4976196289062, "r": 545.1137084960938, "b": 605.6360473632812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. 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"label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569000244140625, "t": 547.9285888671875, "r": 286.3651428222656, "b": 527.0670166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 512.7965698242188, "r": 286.3651123046875, "b": 396.2931213378906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 392.9306640625, "r": 286.3649597167969, "b": 372.068115234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"self_ref": "#/texts/481", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 368.7046813964844, "r": 286.3650817871094, "b": 335.8881530761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/482", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 332.52471923828125, "r": 286.36505126953125, "b": 287.7532043457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/483", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 284.3897705078125, "r": 286.36492919921875, "b": 263.5272216796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/484", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 260.164794921875, "r": 286.3651123046875, "b": 227.34722900390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/485", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 223.98377990722656, "r": 286.3650817871094, "b": 191.16722106933594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.70498657226562, "t": 168.5640869140625, "r": 286.3623962402344, "b": 137.89439392089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } - min { x$_{c}$ } (4)", "text": ""}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 124.6520767211914, "r": 286.36199951171875, "b": 103.07321166992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"self_ref": "#/texts/488", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.11199951171875, "t": 99.70977783203125, "r": 286.3649597167969, "b": 78.84821319580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 581.0687866210938, "r": 545.1151733398438, "b": 536.2962036132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"self_ref": "#/texts/490", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.86199951171875, "t": 532.8977661132812, "r": 545.114990234375, "b": 512.0361938476562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 508.6367492675781, "r": 545.1151123046875, "b": 404.08929443359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/492", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 400.6898498535156, "r": 545.1151733398438, "b": 332.00836181640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/493", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 328.6089172363281, "r": 545.1151733398438, "b": 224.06141662597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/494", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.8620300292969, "t": 220.66197204589844, "r": 545.1168823242188, "b": 187.8454132080078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"self_ref": "#/texts/495", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 184.44696044921875, "r": 545.1150512695312, "b": 163.58441162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/496", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 160.18597412109375, "r": 545.1150512695312, "b": 127.3694076538086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/497", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 123.969970703125, "r": 545.114990234375, "b": 103.10841369628906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/498", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862060546875, "t": 99.70997619628906, "r": 545.1151733398438, "b": 78.84840393066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/499", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.6310729980469, "t": 57.86697006225586, "r": 302.5936584472656, "b": 48.96040725708008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/500", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 716.7916259765625, "r": 88.84658813476562, "b": 707.8850708007812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell."}, {"self_ref": "#/texts/501", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 704.8366088867188, "r": 286.3649597167969, "b": 683.9750366210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell."}, {"self_ref": "#/texts/502", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.11199951171875, "t": 680.8369140625, "r": 286.364990234375, "b": 660.2941284179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below."}, {"self_ref": "#/texts/503", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.340999603271484, "t": 289.9436340332031, "r": 273.1334228515625, "b": 281.0370788574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header."}, {"self_ref": "#/texts/504", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/505", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.86199951171875, "t": 485.4016418457031, "r": 545.1151123046875, "b": 464.54010009765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells."}, {"self_ref": "#/texts/506", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.3429870605469, "t": 111.50663757324219, "r": 541.63232421875, "b": 102.60006713867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells."}, {"self_ref": "#/texts/507", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.11199951171875, "t": 435.2296447753906, "r": 286.3650817871094, "b": 414.36810302734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells."}, {"self_ref": "#/texts/508", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.61899948120117, "t": 120.181640625, "r": 281.85589599609375, "b": 111.27507781982422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing."}, {"self_ref": "#/texts/509", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.6309814453125, "t": 57.86663818359375, "r": 302.59356689453125, "b": 48.96007537841797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/510", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.7900085449219, "t": 420.3156433105469, "r": 538.1852416992188, "b": 411.4090881347656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table."}, {"self_ref": "#/texts/511", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.9849853515625, "t": 108.45364379882812, "r": 508.9893493652344, "b": 99.54707336425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text."}, {"self_ref": "#/texts/512", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.23300170898438, "t": 147.64862060546875, "r": 252.24224853515625, "b": 138.7420654296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table."}, {"self_ref": "#/texts/513", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.6309814453125, "t": 57.86665725708008, "r": 302.59356689453125, "b": 48.9600944519043, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/514", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.8619689941406, "t": 139.0646514892578, "r": 545.1151123046875, "b": 118.20308685302734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact."}, {"self_ref": "#/texts/515", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.11199951171875, "t": 283.6626281738281, "r": 545.1138305664062, "b": 262.80108642578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure."}, {"self_ref": "#/texts/516", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 292.6309814453125, "t": 57.866641998291016, "r": 302.59356689453125, "b": 48.960079193115234, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 315.65362548828125, "t": 563.276611328125, "r": 537.1475219726562, "b": 489.1985778808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/13"}, {"cref": "#/texts/14"}, 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diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.md b/tests/data/groundtruth/docling_v2/2203.01017v2.md
index 3c5e3d23..4a15137f 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.md
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.md
@@ -52,11 +52,11 @@ To meet the design criteria listed above, we developed a new model called TableF
 
 The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe
 
-its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.
+its results &amp; performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.
 
 ## 2. Previous work and State of the Art
 
-Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.
+Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. &gt; 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.
 
 Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.
 
@@ -115,7 +115,7 @@ Given the image of a table, TableFormer is able to predict: 1) a sequence of tok
 
 ## 4.1. Model architecture.
 
-We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.
+We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' &lt; td &gt; ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' &lt; ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' &gt; '. The hidden state attached to ' &lt; ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.
 
 CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-
 
@@ -123,7 +123,7 @@ Figure 3: TableFormer takes in an image of the PDF and creates bounding box and
 
 <!-- image -->
 
-Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.
+Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' &lt; td &gt; ', ' &lt; ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.
 
 <!-- image -->
 
@@ -133,7 +133,7 @@ Structure Decoder. The transformer architecture of this component is based on th
 
 The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.
 
-Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.
+Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the &lt; td &gt; ' and ' &lt; ' HTML structure tags become the object query.
 
 The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-
 
@@ -141,13 +141,13 @@ tention encoding is then multiplied to the encoded image to produce a feature fo
 
 The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.
 
-Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$\_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$\_{box}$ . l$\_{box}$ consists of the generally used l$\_{1}$ loss for object detection and the IoU loss ( l$\_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
+Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
 
 The loss used to train the TableFormer can be defined as following:
 
-l$\_{box}$ = λ$\_{iou}$l$\_{iou}$ + λ$\_{l}$$\_{1}$ l = λl$\_{s}$ + (1 - λ ) l$\_{box}$ (1)
+<!-- formula-not-decoded -->
 
-where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$\_{1}$ ∈$\_{R}$ are hyper-parameters.
+where λ ∈ [0, 1], and λ$_{iou}$, λ$_{l}$$\_{1}$ ∈$\_{R}$ are hyper-parameters.
 
 ## 5. Experimental Results
 
@@ -155,7 +155,7 @@ where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$\_{1}$ ∈$\_{R}$ are hyper-para
 
 TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:
 
-Image width and height ≤ 1024 pixels Structural tags length ≤ 512 tokens. (2)
+<!-- formula-not-decoded -->
 
 Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved
 
@@ -177,9 +177,9 @@ We also share our baseline results on the challenging SynthTabNet dataset. Throu
 
 The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:
 
-TEDS ( T$\_{a}$, T$\_{b}$ ) = 1 - EditDist ( T$\_{a}$, T$\_{b}$ ) max ( | T$\_{a}$ | , | T$\_{b}$ | ) (3)
+<!-- formula-not-decoded -->
 
-where T$\_{a}$ and T$\_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
+where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
 
 ## 5.4. Quantitative Analysis
 
@@ -277,7 +277,7 @@ Figure 6: An example of TableFormer predictions (bounding boxes and structure) f
 
 We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.
 
-## 6. Future Work & Conclusion
+## 6. Future Work &amp; Conclusion
 
 In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.
 
@@ -377,9 +377,9 @@ Here is a step-by-step description of the prediction postprocessing:
 - 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.
 - 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:
 
-alignment = arg min c { D$\_{c}$ } D$\_{c}$ = max { x$\_{c}$ } - min { x$\_{c}$ } (4)
+<!-- formula-not-decoded -->
 
-where c is one of { left, centroid, right } and x$\_{c}$ is the xcoordinate for the corresponding point.
+where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.
 
 - 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-
 
diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
index b58d1e90..fa6caa34 100644
--- a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
+++ b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", 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437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868066072463989, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.75862056016922, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.48547354340553284, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.909633457660675, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671402931213, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882684707642, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501362085342407, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "picture", "bbox": {"l": 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501362085342407, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. 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Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045888543128967, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773135542869568, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}], "body": [{"label": "section_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868066072463989, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers."}, {"label": "section_header", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.75862056016922, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research"}, {"label": "key_value_region", "id": 25, "page_no": 0, "cluster": {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.48547354340553284, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.909633457660675, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671402931213, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882684707642, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317678213119507, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841896891593933, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724017858505249, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549347400665283, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501362085342407, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78167724609375, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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"caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214613437652588, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045888543128967, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773135542869568, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656873345375061, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838619828224182, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855936169624329, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820109009742737, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822154641151428, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9822708964347839, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802958369255066, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806389212608337, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742556214332581, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.8953585624694824, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774291515350342, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428720474243164, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.9871960878372192, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869063496589661, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843320846557617, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0", "bbox": {"l": 316.57742, "t": 152.47400000000005, "r": 320.3934, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "7", "bbox": {"l": 312.19775, "t": 120.57050000000004, "r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.1036071777344, "t": 78.44092559814453, "r": 550.3890380859375, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746916890144348, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": 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table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879810571670532, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.1036071777344, "t": 78.44092559814453, "r": 550.3890380859375, "b": 250.60989379882812, "coord_origin": "TOPLEFT"}, "confidence": 0.9746916890144348, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 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The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767213225364685, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878448843955994, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.987509548664093, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423060417175293, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. 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the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879810571670532, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715605139732361, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 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While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": 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"children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, 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"b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9668342471122742, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466321468353271, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9614067077636719, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.9880395531654358, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9874395728111267, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876185655593872, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9870707392692566, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.6773376464844, "t": 73.19310760498047, "r": 542.9581909179688, "b": 155.22084045410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9777666330337524, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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{"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.9791285991668701, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9049099087715149, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466321468353271, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.988465428352356, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.9376939535140991, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878638386726379, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9786655306816101, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8562641739845276, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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"coord_origin": "TOPLEFT"}}, {"id": 30, "text": "provide full description of", "bbox": {"l": 431.1738, "t": 157.80517999999995, "r": 498.87753000000004, "b": 163.54578000000004, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the table structure", "bbox": {"l": 440.5289, "t": 163.92516999999998, "r": 489.51827999999995, "b": 169.66576999999995, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Structure Tags", "bbox": {"l": 328.37479, "t": 178.25385000000006, "r": 367.72333, "b": 183.99445000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "BBoxes in sync", "bbox": {"l": 331.84451, "t": 123.90886999999998, "r": 373.67963, "b": 129.64948000000015, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "with tag sequence", "bbox": {"l": 331.84451, "t": 129.00885000000017, "r": 381.17786, "b": 134.74945000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Encoder", "bbox": {"l": 196.62633, "t": 88.11621000000002, "r": 219.42332, "b": 93.85681, "coord_origin": "TOPLEFT"}}, {"id": 36, 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The bounding boxes grabs the content from the PDF and inserts it in the structure.", "bbox": {"l": 50.111992, "t": 216.06035999999995, "r": 436.0134, "b": 224.96691999999996, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Input Image", "bbox": {"l": 74.253464, "t": 258.21472000000006, "r": 101.75846, "b": 264.17474000000004, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tokenised Tags", "bbox": {"l": 122.29972, "t": 258.34520999999995, "r": 157.83972, "b": 264.30524, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Multi-Head Attention", "bbox": {"l": 78.549347, "t": 371.38579999999996, "r": 125.68359000000001, "b": 377.04782, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Add", "bbox": {"l": 78.513298, "t": 391.31857, "r": 84.644547, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "& Normalisation", "bbox": {"l": 116.52705, "t": 391.31857, "r": 125.11079999999998, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Feed Forward Network", "bbox": 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30531311035156, "t": 77.91128540039062, "r": 519.980224609375, "b": 183.70115661621094, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Names", "bbox": {"l": 82.140205, "t": 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719565868377686, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30531311035156, "t": 77.91128540039062, "r": 519.980224609375, "b": 183.70115661621094, "coord_origin": "TOPLEFT"}, "confidence": 0.9296937584877014, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869197010993958, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719565868377686, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363928437232971, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363928437232971, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868088960647583, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860329031944275, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.987363874912262, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363928437232971, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538504481315613, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.8441833853721619, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880929589271545, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808167457581, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820405602455139, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": 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"TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554281830787659, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1356506347656, "r": 283.0443420410156, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": 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We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1356506347656, "r": 283.0443420410156, "b": 582.397705078125, "coord_origin": "TOPLEFT"}, "confidence": 0.989250659942627, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209139466285706, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433356404304504, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775566458702087, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "93.6", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 308.862, "t": 656.86136, "r": 545.11517, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}, "confidence": 0.954140305519104, "cells": [{"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787977695465088, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554281830787659, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500427842140198, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.973533034324646, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588839411735535, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855115413665771, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209139466285706, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433356404304504, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854633808135986, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713152647018433, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4066467285156, "t": 247.87643432617188, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691706299781799, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835003614425659, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835083007812, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775566458702087, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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"TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.598688006401062, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": 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different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. 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Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442477226257324, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318730592727661, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8709819316864014, "cells": [{"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5844421386719, "t": 98.65103912353516, "r": 554.8255615234375, "b": 180.62567138671875, "coord_origin": "TOPLEFT"}, "confidence": 0.7697678208351135, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71235656738281, "r": 301.6350402832031, "b": 187.57875061035156, "coord_origin": "TOPLEFT"}, "confidence": 0.7873173952102661, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.598688006401062, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4644930958747864, "cells": [{"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.462892085313797, "cells": [{"id": 3, "text": "Example table from FinTabNet:", "bbox": {"l": 304.83081, "t": 94.28112999999996, 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"label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153726696968079, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561256170272827, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852355718612671, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436545968055725, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875592589378357, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442477226257324, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318730592727661, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In Andrea Vedaldi, Horst Bischof, Thomas", "bbox": {"l": 328.78101, "t": 75.88347999999996, "r": 545.11346, "b": 83.89948000000015, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "Brox, and Jan-Michael Frahm, editors,", "bbox": {"l": 328.78101, "t": 86.84149000000002, "r": 472.30618, "b": 94.85748000000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Computer Vision -", "bbox": {"l": 475.88501, "t": 86.92218000000003, "r": 545.11456, "b": 94.65125, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "ECCV 2020", "bbox": {"l": 328.78101, "t": 97.88116000000002, "r": 371.92734, "b": 105.61023, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": ", pages 564-580, Cham, 2020. 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A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497083306312561, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645246505737, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826023578643799, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335851669311523, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596059918403625, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261794090270996, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642950296401978, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.969999372959137, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710657596588135, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.9549404978752136, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Complex", "bbox": {"l": 287.89441, "t": 141.71063000000004, "r": 310.14572, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Non", "bbox": {"l": 289.23572, "t": 93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 147.5909423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.6033807992935181, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, 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[{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 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713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 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"Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, 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"b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 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"bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260513186454773, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74853515625, "r": 544.9381713867188, "b": 147.5909423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.6033807992935181, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": 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"TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629554152488708, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767083525657654, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.9347671866416931, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.828071117401123, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646763205528259, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391403198242188, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491713047027588, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587168097496033, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298413991928101, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260513186454773, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.894230306148529, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423394799232483, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562010169029236, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719936609268188, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126079082489014, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459476232528687, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716901302337646, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584908843040466, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590751051902771, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971200108528137, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126595258712769, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. 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+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 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437.5936, "r": 465.88101, "b": 447.724, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3", "bbox": {"l": 384.0329, "t": 252.67895999999996, "r": 390.03769, "b": 262.80939, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868067860603333, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586215734481812, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.4854728579521179, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096331596374512, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671402931213, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "picture", "bbox": {"l": 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This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214619398117065, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}, {"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045893311500549, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773143887519836, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}], "body": [{"label": "section_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8868067860603333, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers.", "bbox": {"l": 96.301003, "t": 107.03412000000003, "r": 498.92708999999996, "b": 119.93133999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers."}, {"label": "section_header", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7586215734481812, "cells": [{"id": 1, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "bbox": {"l": 142.47701, "t": 146.68535999999995, "r": 452.75027, "b": 157.37334999999996, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "IBM Research", "bbox": {"l": 262.918, "t": 160.63239, "r": 332.30597, "b": 171.32037000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research"}, {"label": "key_value_region", "id": 25, "page_no": 0, "cluster": {"id": 25, "label": "key_value_region", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.4854728579521179, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": [{"id": 7, "label": "text", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 378.73257, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9096331596374512, "cells": [{"id": 3, "text": "{", "bbox": {"l": 208.123, "t": 175.96123999999998, "r": 212.73083, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ahn,nli,mly,taa", "bbox": {"l": 212.73, "t": 177.08203000000003, "r": 293.42761, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "}", "bbox": {"l": 293.42798, "t": 175.96123999999998, "r": 298.0358, "b": 184.42553999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "@zurich.ibm.com", "bbox": {"l": 298.03497, "t": 177.08203000000003, "r": 378.73257, "b": 184.00409000000002, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9258671402931213, "cells": [{"id": 7, "text": "Abstract", "bbox": {"l": 145.99498, "t": 215.48297000000002, "r": 190.48029, "b": 226.23071000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111977, "t": 241.39508, "r": 286.36511, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9838882088661194, "cells": [{"id": 8, "text": "Tables organize valuable content in a concise and com-", "bbox": {"l": 62.066978, "t": 241.39508, "r": 286.36493, "b": 249.98284999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "pact representation. This content is extremely valuable for", "bbox": {"l": 50.111977, "t": 253.3501, "r": 286.36508, "b": 261.93787, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "systems such as search engines, Knowledge Graph\u2019s, etc,", "bbox": {"l": 50.111977, "t": 265.30511, "r": 286.36508, "b": 273.89288, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "since they enhance their predictive capabilities. Unfortu-", "bbox": {"l": 50.111977, "t": 277.26111000000003, "r": 286.36505, "b": 285.84888, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "nately, tables come in a large variety of shapes and sizes.", "bbox": {"l": 50.111977, "t": 289.21609, "r": 286.36505, "b": 297.80386, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Furthermore, they can have complex column/row-header", "bbox": {"l": 50.111977, "t": 301.17108, "r": 286.36505, "b": 309.75884999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "configurations, multiline rows, different variety of separa-", "bbox": {"l": 50.111977, "t": 313.12607, "r": 286.36508, "b": 321.71384, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "tion lines, missing entries, etc. As such, the correct iden-", "bbox": {"l": 50.111977, "t": 325.08105, "r": 286.36508, "b": 333.66882, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "tification of the table-structure from an image is a non-", "bbox": {"l": 50.111977, "t": 337.03604, "r": 286.36505, "b": 345.62381, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "trivial task. In this paper, we present a new table-structure", "bbox": {"l": 50.111977, "t": 348.99203, "r": 286.36508, "b": 357.5798, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "identification model. The latter improves the latest end-to-", "bbox": {"l": 50.111977, "t": 360.94701999999995, "r": 286.36505, "b": 369.53479, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "end deep learning model (i.e. encoder-dual-decoder from", "bbox": {"l": 50.111977, "t": 372.90201, "r": 286.36508, "b": 381.48978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "PubTabNet) in two significant ways. First, we introduce a", "bbox": {"l": 50.111977, "t": 384.85699, "r": 286.36505, "b": 393.44476, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "new object detection decoder for table-cells. In this way,", "bbox": {"l": 50.111977, "t": 396.81198, "r": 286.36511, "b": 405.39975000000004, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "we can obtain the content of the table-cells from program-", "bbox": {"l": 50.111977, "t": 408.76697, "r": 286.36508, "b": 417.35474, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "matic PDF\u2019s directly from the PDF source and avoid the", "bbox": {"l": 50.111977, "t": 420.72296000000006, "r": 286.36505, "b": 429.31073, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "training of the custom OCR decoders.", "bbox": {"l": 50.111977, "t": 432.67795, "r": 207.23216, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "This architectural", "bbox": {"l": 214.09639, "t": 432.67795, "r": 286.36508, "b": 441.26572, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "change leads to more accurate table-content extraction and", "bbox": {"l": 50.111977, "t": 444.63293, "r": 286.36508, "b": 453.2207, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "allows us to tackle non-english tables. Second, we replace", "bbox": {"l": 50.111977, "t": 456.58792000000005, "r": 286.36505, "b": 465.17569, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the LSTM decoders with transformer based decoders. This", "bbox": {"l": 50.111977, "t": 468.54291, "r": 286.36505, "b": 477.13068, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "upgrade improves significantly the previous state-of-the-art", "bbox": {"l": 50.111977, "t": 480.4989, "r": 286.36508, "b": 489.08667, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "bbox": {"l": 50.111977, "t": 492.45389, "r": 286.36505, "b": 501.04166, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "simple tables and from 88.7% to 95% on complex tables.", "bbox": {"l": 50.111977, "t": 504.40887, "r": 276.65152, "b": 512.9966400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph\u2019s, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF\u2019s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.111977, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"id": 32, "text": "1.", "bbox": {"l": 50.111977, "t": 539.94276, "r": 58.121296, "b": 550.69049, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Introduction", "bbox": {"l": 68.800385, "t": 539.94276, "r": 126.94804, "b": 550.69049, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111977, "t": 560.7832, "r": 286.36508, "b": 713.151779, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"id": 34, "text": "The occurrence of tables in documents is ubiquitous.", "bbox": {"l": 62.066978, "t": 560.7832, "r": 286.36496, "b": 569.68976, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "They often summarise quantitative or factual data, which is", "bbox": {"l": 50.111977, "t": 572.7382, "r": 286.36508, "b": 581.64476, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "cumbersome to describe in verbose text but nevertheless ex-", "bbox": {"l": 50.111977, "t": 584.69321, "r": 286.36505, "b": 593.5997600000001, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "tremely valuable. Unfortunately, this compact representa-", "bbox": {"l": 50.111977, "t": 596.6492000000001, "r": 286.36505, "b": 605.55576, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "tion is often not easy to parse by machines. There are many", "bbox": {"l": 50.111977, "t": 608.6042, "r": 286.36505, "b": 617.51076, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "implicit conventions used to obtain a compact table repre-", "bbox": {"l": 50.111977, "t": 620.5592, "r": 286.36505, "b": 629.46576, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "sentation. For example, tables often have complex column-", "bbox": {"l": 50.111977, "t": 632.51421, "r": 286.36508, "b": 641.42076, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "and row-headers in order to reduce duplicated cell content.", "bbox": {"l": 50.111977, "t": 644.46921, "r": 286.36508, "b": 653.37576, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Lines of different shapes and sizes are leveraged to separate", "bbox": {"l": 50.111977, "t": 656.42421, "r": 286.36502, "b": 665.33077, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "content or indicate a tree structure. Additionally, tables can", "bbox": {"l": 50.111977, "t": 668.3802000000001, "r": 286.36505, "b": 677.28677, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "also have empty/missing table-entries or multi-row textual", "bbox": {"l": 50.111977, "t": 680.33521, "r": 286.36505, "b": 689.2417800000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "table-entries. Fig. 1 shows a table which presents all these", "bbox": {"l": 50.111977, "t": 692.290207, "r": 286.36505, "b": 701.196777, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "issues.", "bbox": {"l": 50.111977, "t": 704.245209, "r": 76.403275, "b": 713.151779, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.56702, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"id": 47, "text": "a.", "bbox": {"l": 315.56702, "t": 218.00684, "r": 324.01007, "b": 226.75482, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Picture of a table:", "bbox": {"l": 328.2316, "t": 218.00684, "r": 408.4407, "b": 226.75482, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 486.40194999999994, "b": 333.2428, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"id": 49, "text": "b.", "bbox": {"l": 315.56702, "t": 313.69478999999995, "r": 325.05786, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Red-annotation of bounding boxes,", "bbox": {"l": 329.80325, "t": 313.69478999999995, "r": 486.40194999999994, "b": 322.44281, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Blue-predictions by TableFormer", "bbox": {"l": 326.46252, "t": 324.49478, "r": 472.47411999999997, "b": 333.2428, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.56702, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"id": 52, "text": "c.", "bbox": {"l": 315.56702, "t": 420.1828, "r": 324.81039, "b": 428.93082, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Structure predicted by TableFormer:", "bbox": {"l": 329.4321, "t": 420.1828, "r": 491.1912500000001, "b": 428.93082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"id": 54, "text": "1", "bbox": {"l": 408.14752, "t": 342.82828, "r": 412.54001, "b": 351.61322, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0", "bbox": {"l": 356.11011, "t": 341.57217, "r": 360.50259, "b": 350.35712, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 500.6777, "t": 340.93768, "r": 505.0701900000001, "b": 349.7226299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3", "bbox": {"l": 356.13382, "t": 351.74789, "r": 360.52631, "b": 360.53284, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "4", "bbox": {"l": 402.53992, "t": 355.8765, "r": 406.9324, "b": 364.66144, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "5", "bbox": {"l": 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false, "row_section": false}]}, {"label": "caption", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 308.862, "t": 514.50037, "r": 545.11517, "b": 559.2729, "coord_origin": "TOPLEFT"}, "confidence": 0.9214619398117065, "cells": [{"id": 106, "text": "Figure 1:", "bbox": {"l": 308.862, "t": 514.50037, "r": 345.73361, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "Picture of a table with subtle, complex features", "bbox": {"l": 353.17566, "t": 514.50037, "r": 545.11511, "b": 523.40692, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "such as (1) multi-column headers, (2) cell with multi-row", "bbox": {"l": 308.862, "t": 526.45535, "r": 545.11511, "b": 535.3619100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "text and (3) cells with no content. Image from PubTabNet", "bbox": {"l": 308.862, "t": 538.41035, "r": 545.11517, "b": 547.31691, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "evaluation set, filename: \u2018PMC2944238 004 02\u2019.", "bbox": {"l": 308.862, "t": 550.36635, "r": 505.6917700000001, "b": 559.2729, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: \u2018PMC2944238 004 02\u2019."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 584.40936, "r": 545.11517, "b": 665.04693, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"id": 111, "text": "Recently, significant progress has been made with vi-", "bbox": {"l": 320.81699, "t": 584.40936, "r": 545.11493, "b": 593.31592, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sion based approaches to extract tables in documents. For", "bbox": {"l": 308.862, "t": 596.36436, "r": 545.11517, "b": 605.2709199999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "the sake of completeness, the issue of table extraction from", "bbox": {"l": 308.862, "t": 608.31937, "r": 545.11511, "b": 617.22592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "documents is typically decomposed into two separate chal-", "bbox": {"l": 308.862, "t": 620.27437, "r": 545.11505, "b": 629.18092, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "lenges, i.e.", "bbox": {"l": 308.862, "t": 632.23036, "r": 353.6937, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "(1)", "bbox": {"l": 362.11209, "t": 632.23036, "r": 374.66617, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "finding the location of the table(s) on a", "bbox": {"l": 377.35785, "t": 632.23036, "r": 545.11505, "b": 641.13692, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "document-page and (2) finding the structure of a given table", "bbox": {"l": 308.862, "t": 644.18536, "r": 545.11517, "b": 653.09192, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "in the document.", "bbox": {"l": 308.862, "t": 656.14037, "r": 375.55167, "b": 665.04693, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.38036, "r": 545.11511, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"id": 120, "text": "The first problem is called table-location and has been", "bbox": {"l": 320.81699, "t": 668.38036, "r": 545.11493, "b": 677.28693, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "bbox": {"l": 308.862, "t": 680.33536, "r": 545.11511, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of-the-art object-detection networks (e.g. YOLO and later", "bbox": {"l": 308.862, "t": 692.290359, "r": 545.11511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "on Mask-RCNN [9]). For all practical purposes, it can be", "bbox": {"l": 308.862, "t": 704.245361, "r": 545.11499, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8045893311500549, "cells": [{"id": 124, "text": "1", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8773143887519836, "cells": [{"id": 125, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "bbox": {"l": 18.340221, "t": 207.82001000000002, "r": 36.339779, "b": 560.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802963733673096, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802963733673096, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"id": 0, "text": "considered as a solved problem, given enough ground-truth", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36505, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "data to train on.", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 112.64721999999999, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "considered as a solved problem, given enough ground-truth data to train on."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.57141000000001, "r": 286.36514, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"id": 2, "text": "The second problem is called table-structure decompo-", "bbox": {"l": 62.067001, "t": 99.57141000000001, "r": 286.36496, "b": 108.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "sition.", "bbox": {"l": 50.112, "t": 111.52643, "r": 74.749512, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The latter is a long standing problem in the com-", "bbox": {"l": 81.334793, "t": 111.52643, "r": 286.36514, "b": 120.43297999999993, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "munity of document understanding [6, 4, 14]. Contrary to", "bbox": {"l": 50.112, "t": 123.48145, "r": 286.36511, "b": 132.38800000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the table-location problem, there are no commonly used ap-", "bbox": {"l": 50.112, "t": 135.43646, "r": 286.36511, "b": 144.34302000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "proaches that can easily be re-purposed to solve this prob-", "bbox": {"l": 50.112, "t": 147.39246000000003, "r": 286.36505, "b": 156.29900999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "lem. Lately, a set of new model-architectures has been pro-", "bbox": {"l": 50.112, "t": 159.34747000000004, "r": 286.36511, "b": 168.25402999999994, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "posed by the community to address table-structure decom-", "bbox": {"l": 50.112, "t": 171.30249000000003, "r": 286.36508, "b": 180.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "position [37, 36, 18, 20]. All these models have some weak-", "bbox": {"l": 50.112, "t": 183.25751000000002, "r": 286.36511, "b": 192.16405999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "nesses (see Sec. 2). The common denominator here is the", "bbox": {"l": 50.112, "t": 195.21252000000004, "r": 286.36508, "b": 204.11908000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "reliance on textual features and/or the inability to provide", "bbox": {"l": 50.112, "t": 207.16754000000003, "r": 286.36514, "b": 216.07410000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "the bounding box of each table-cell in the original image.", "bbox": {"l": 50.112, "t": 219.12354000000005, "r": 278.66397, "b": 228.03008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.53156, "r": 286.36511, "b": 371.94507, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"id": 14, "text": "In this paper, we want to address these weaknesses and", "bbox": {"l": 62.067001, "t": 231.53156, "r": 286.36493, "b": 240.43811000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "present a robust table-structure decomposition algorithm.", "bbox": {"l": 50.112, "t": 243.48657000000003, "r": 286.36511, "b": 252.39313000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "The design criteria for our model are the following. First,", "bbox": {"l": 50.112, "t": 255.44159000000002, "r": 286.36511, "b": 264.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "we want our algorithm to be language agnostic. In this way,", "bbox": {"l": 50.112, "t": 267.39661, "r": 286.36502, "b": 276.30316000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "we can obtain the structure of any table, irregardless of the", "bbox": {"l": 50.112, "t": 279.35155999999995, "r": 286.36508, "b": 288.25815, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "language.", "bbox": {"l": 50.112, "t": 291.30759, "r": 88.567635, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Second, we want our algorithm to leverage as", "bbox": {"l": 95.501602, "t": 291.30759, "r": 286.36505, "b": 300.21414, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "much data as possible from the original PDF document. For", "bbox": {"l": 50.112, "t": 303.26257, "r": 286.36508, "b": 312.16913, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "programmatic PDF documents, the text-cells can often be", "bbox": {"l": 50.112, "t": 315.21756, "r": 286.36511, "b": 324.12411, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "extracted much faster and with higher accuracy compared", "bbox": {"l": 50.112, "t": 327.17255, "r": 286.36505, "b": 336.0791, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "to OCR methods. Last but not least, we want to have a di-", "bbox": {"l": 50.112, "t": 339.12753, "r": 286.36511, "b": 348.03409, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "rect link between the table-cell and its bounding box in the", "bbox": {"l": 50.112, "t": 351.08353, "r": 286.36508, "b": 359.99008, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "image.", "bbox": {"l": 50.112, "t": 363.03851, "r": 76.951241, "b": 371.94507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.4465, "r": 286.36658, "b": 432.173, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"id": 27, "text": "To meet the design criteria listed above, we developed a", "bbox": {"l": 62.067001, "t": 375.4465, "r": 286.36499, "b": 384.35306, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "new model called", "bbox": {"l": 50.112, "t": 387.40149, "r": 120.98594, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "TableFormer", "bbox": {"l": 123.901, "t": 387.28192, "r": 179.7314, "b": 396.23830999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and a synthetically gener-", "bbox": {"l": 182.646, "t": 387.40149, "r": 286.36658, "b": 396.30804, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ated table structure dataset called", "bbox": {"l": 50.112, "t": 399.35648, "r": 181.75778, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "SynthTabNet", "bbox": {"l": 184.104, "t": 399.23690999999997, "r": 240.2034, "b": 408.1933, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "$^{1}$. In partic-", "bbox": {"l": 240.20401, "t": 399.35648, "r": 286.36069, "b": 408.26302999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ular, our contributions in this work can be summarised as", "bbox": {"l": 50.112015, "t": 411.31146, "r": 286.36511, "b": 420.21802, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "follows:", "bbox": {"l": 50.112015, "t": 423.26645, "r": 82.520355, "b": 432.173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet $^{1}$. In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569016, "t": 444.43188, "r": 286.3649, "b": 489.32297, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"id": 36, "text": "\u2022", "bbox": {"l": 61.569016, "t": 444.55145, "r": 70.741714, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "We propose", "bbox": {"l": 73.034889, "t": 444.55145, "r": 117.10054, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "TableFormer", "bbox": {"l": 119.59001, "t": 444.43188, "r": 175.42041, "b": 453.38828, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": ", a transformer based model", "bbox": {"l": 175.42102, "t": 444.55145, "r": 286.36453, "b": 453.45801, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "that predicts tables structure and bounding boxes for", "bbox": {"l": 70.037018, "t": 456.50644000000005, "r": 286.3649, "b": 465.41299, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "the table content simultaneously in an end-to-end ap-", "bbox": {"l": 70.037018, "t": 468.46143, "r": 286.3649, "b": 477.36798, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "proach.", "bbox": {"l": 70.037018, "t": 480.41641, "r": 99.635902, "b": 489.32297, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569016, "t": 502.03384, "r": 286.3649, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"id": 43, "text": "\u2022", "bbox": {"l": 61.569016, "t": 502.15341, "r": 71.619438, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Across all benchmark datasets", "bbox": {"l": 74.132042, "t": 502.15341, "r": 196.10396, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "TableFormer", "bbox": {"l": 200.31001, "t": 502.03384, "r": 256.14041, "b": 510.99023, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "signif-", "bbox": {"l": 260.35001, "t": 502.15341, "r": 286.36237, "b": 511.05997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "icantly outperforms existing state-of-the-art metrics,", "bbox": {"l": 70.037003, "t": 514.1084000000001, "r": 286.3649, "b": 523.01495, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "while being much more efficient in training and infer-", "bbox": {"l": 70.037003, "t": 526.06439, "r": 286.36487, "b": 534.97095, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ence to existing works.", "bbox": {"l": 70.037003, "t": 538.0193899999999, "r": 161.65305, "b": 546.9259500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 61.569, "t": 559.63684, "r": 286.36493, "b": 592.57295, "coord_origin": "TOPLEFT"}, "confidence": 0.9802963733673096, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 61.569, "t": 559.75639, "r": 71.115913, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "We present", "bbox": {"l": 73.502647, "t": 559.75639, "r": 116.71199, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "SynthTabNet", "bbox": {"l": 121.583, "t": 559.63684, "r": 177.68239, "b": 568.59322, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a synthetically generated", "bbox": {"l": 182.55301, "t": 559.75639, "r": 286.36328, "b": 568.66295, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "dataset, with various appearance styles and complex-", "bbox": {"l": 70.03701, "t": 571.7114, "r": 286.36493, "b": 580.6179500000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "ity.", "bbox": {"l": 70.03701, "t": 583.6664000000001, "r": 82.400597, "b": 592.57295, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 286.36508, "b": 638.22095, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"id": 56, "text": "\u2022", "bbox": {"l": 61.569008000000004, "t": 605.4034, "r": 72.332527, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "An augmented dataset based on PubTabNet [37],", "bbox": {"l": 75.023399, "t": 605.4034, "r": 286.36508, "b": 614.30995, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "FinTabNet [36], and TableBank [17] with generated", "bbox": {"l": 70.03701, "t": 617.3584, "r": 286.36487, "b": 626.26495, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "ground-truth for reproducibility.", "bbox": {"l": 70.03701, "t": 629.31439, "r": 198.05641, "b": 638.22095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112007, "t": 650.59839, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"id": 60, "text": "The paper is structured as follows. In Sec. 2, we give", "bbox": {"l": 62.067009000000006, "t": 650.59839, "r": 286.36496, "b": 659.50494, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "a brief overview of the current state-of-the-art. In Sec. 3,", "bbox": {"l": 50.112007, "t": 662.55339, "r": 286.36511, "b": 671.45995, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we describe the datasets on which we train. In Sec. 4, we", "bbox": {"l": 50.112007, "t": 674.50839, "r": 286.36511, "b": 683.41496, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "introduce the TableFormer model-architecture and describe", "bbox": {"l": 50.112007, "t": 686.46339, "r": 286.36511, "b": 695.369957, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"id": 64, "text": "$^{1}$https://github.com/IBM/SynthTabNet", "bbox": {"l": 60.97100100000001, "t": 705.596275, "r": 183.73055, "b": 712.721542, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"id": 65, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "scribe how this new model-architecture can be re-purposed", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11505, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for other tasks in the computer-vision community.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 508.08417000000003, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"id": 68, "text": "2.", "bbox": {"l": 308.862, "t": 121.73193000000003, "r": 315.5831, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Previous work and State of the Art", "bbox": {"l": 324.54456, "t": 121.73193000000003, "r": 498.28021, "b": 132.47968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.22136999999998, "r": 545.11517, "b": 330.45502, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"id": 70, "text": "Identifying the structure of a table has been an outstand-", "bbox": {"l": 320.81699, "t": 142.22136999999998, "r": 545.11493, "b": 151.12793, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "ing problem in the document-parsing community, that mo-", "bbox": {"l": 308.862, "t": 154.17638999999997, "r": 545.11505, "b": 163.08294999999998, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tivates many organised public challenges [6, 4, 14].", "bbox": {"l": 308.862, "t": 166.13140999999996, "r": 522.55975, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "The", "bbox": {"l": 529.62323, "t": 166.13140999999996, "r": 545.11505, "b": 175.03796, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "difficulty of the problem can be attributed to a number of", "bbox": {"l": 308.862, "t": 178.08642999999995, "r": 545.11517, "b": 186.99298, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "factors. First, there is a large variety in the shapes and sizes", "bbox": {"l": 308.862, "t": 190.04143999999997, "r": 545.11511, "b": 198.94799999999998, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "of tables.", "bbox": {"l": 308.862, "t": 201.99645999999996, "r": 346.97891, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Such large variety requires a flexible method.", "bbox": {"l": 354.86929, "t": 201.99645999999996, "r": 545.11511, "b": 210.90301999999997, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This is especially true for complex column- and row head-", "bbox": {"l": 308.862, "t": 213.95245, "r": 545.11505, "b": 222.85901, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "ers, which can be extremely intricate and demanding.", "bbox": {"l": 308.862, "t": 225.90747, "r": 530.9184, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "A", "bbox": {"l": 537.92212, "t": 225.90747, "r": 545.11511, "b": 234.81403, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "second factor of complexity is the lack of data with regard", "bbox": {"l": 308.862, "t": 237.86248999999998, "r": 545.11517, "b": 246.76904000000002, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "to table-structure. Until the publication of PubTabNet [37],", "bbox": {"l": 308.862, "t": 249.8175, "r": 545.11511, "b": 258.72406, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "there were no large datasets (i.e.", "bbox": {"l": 308.862, "t": 261.77252, "r": 439.8402699999999, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ">", "bbox": {"l": 444.43999999999994, "t": 261.61310000000003, "r": 452.1889, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "100", "bbox": {"l": 455.89001, "t": 261.61310000000003, "r": 470.83392000000003, "b": 270.45989999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "K tables) that pro-", "bbox": {"l": 470.83401, "t": 261.77252, "r": 545.11517, "b": 270.67908, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "vided structure information. This happens primarily due to", "bbox": {"l": 308.862, "t": 273.72748, "r": 545.11511, "b": 282.63406, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "the fact that tables are notoriously time-consuming to an-", "bbox": {"l": 308.862, "t": 285.6835, "r": 545.11511, "b": 294.59006, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "notate by hand. However, this has definitely changed in re-", "bbox": {"l": 308.862, "t": 297.63849, "r": 545.11511, "b": 306.54504, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "bbox": {"l": 308.862, "t": 309.59348, "r": 545.11517, "b": 318.50003000000004, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Net [36], TableBank [17] etc.", "bbox": {"l": 308.862, "t": 321.54846, "r": 425.92255, "b": 330.45502, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.56946, "r": 545.11523, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"id": 92, "text": "Before the rising popularity of deep neural networks,", "bbox": {"l": 320.81699, "t": 333.56946, "r": 545.11499, "b": 342.47601, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "the community relied heavily on heuristic and/or statistical", "bbox": {"l": 308.862, "t": 345.52444, "r": 545.11499, "b": 354.43100000000004, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "bbox": {"l": 308.862, "t": 357.47943, "r": 545.11517, "b": 366.38599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "28]. Although such methods work well on constrained ta-", "bbox": {"l": 308.862, "t": 369.43542, "r": 545.11511, "b": 378.34198, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "bles [12], a more data-driven approach can be applied due", "bbox": {"l": 308.862, "t": 381.39041, "r": 545.11505, "b": 390.29697, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "to the advent of convolutional neural networks (CNNs) and", "bbox": {"l": 308.862, "t": 393.3453999999999, "r": 545.11505, "b": 402.25195, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "the availability of large datasets. To the best-of-our knowl-", "bbox": {"l": 308.862, "t": 405.30038, "r": 545.11517, "b": 414.20694, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "edge, there are currently two different types of network ar-", "bbox": {"l": 308.862, "t": 417.25537, "r": 545.11523, "b": 426.16193, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "chitecture that are being pursued for state-of-the-art table-", "bbox": {"l": 308.862, "t": 429.21136000000007, "r": 545.11511, "b": 438.11792, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "structure identification.", "bbox": {"l": 308.862, "t": 441.16635, "r": 401.28503, "b": 450.0729099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 453.06778, "r": 545.11688, "b": 713.151848, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"id": 102, "text": "Image-to-Text networks", "bbox": {"l": 320.81699, "t": 453.06778, "r": 423.26236, "b": 462.02417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": ": In this type of network, one", "bbox": {"l": 423.26697, "t": 453.18735, "r": 545.10956, "b": 462.0939, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "predicts a sequence of tokens starting from an encoded", "bbox": {"l": 308.86197, "t": 465.14233, "r": 545.11511, "b": 474.04889, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "image.", "bbox": {"l": 308.86197, "t": 477.09732, "r": 335.7012, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Such sequences of tokens can be HTML table", "bbox": {"l": 345.85309, "t": 477.09732, "r": 545.11505, "b": 486.00388, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "tags [37, 17] or LaTeX symbols[10]. The choice of sym-", "bbox": {"l": 308.86197, "t": 489.05231, "r": 545.11493, "b": 497.95886, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "bols is ultimately not very important, since one can be trans-", "bbox": {"l": 308.86197, "t": 501.00729, "r": 545.11499, "b": 509.91385, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "formed into the other. There are however subtle variations", "bbox": {"l": 308.86197, "t": 512.9632899999999, "r": 545.11505, "b": 521.8698400000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "in the Image-to-Text networks. The easiest network archi-", "bbox": {"l": 308.86197, "t": 524.91827, "r": 545.11505, "b": 533.82483, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tectures are \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 536.87328, "r": 420.94119, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "\u2192", "bbox": {"l": 423.59497, "t": 536.1559599999999, "r": 433.5575600000001, "b": 545.56065, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "text-decoder\u201d (IETD), sim-", "bbox": {"l": 436.21198, "t": 536.87328, "r": 545.11316, "b": 545.77983, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ilar to network architectures that try to provide captions to", "bbox": {"l": 308.86197, "t": 548.82828, "r": 545.11511, "b": 557.73483, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "images [32]. In these IETD networks, one expects as output", "bbox": {"l": 308.86197, "t": 560.78328, "r": 545.11493, "b": 569.68983, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "the LaTeX/HTML string of the entire table, i.e. the sym-", "bbox": {"l": 308.86197, "t": 572.73828, "r": 545.11499, "b": 581.6448399999999, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "bols necessary for creating the table with the content of the", "bbox": {"l": 308.86197, "t": 584.69427, "r": 545.11505, "b": 593.60083, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "table. Another approach is the \u201cimage-encoder", "bbox": {"l": 308.86197, "t": 596.6492800000001, "r": 497.07541, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "\u2192", "bbox": {"l": 499.80496, "t": 595.93196, "r": 509.76755, "b": 605.33665, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "dual de-", "bbox": {"l": 512.50098, "t": 596.6492800000001, "r": 545.10852, "b": 605.55583, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder\u201d (IEDD) networks. In these type of networks, one has", "bbox": {"l": 308.86197, "t": 608.60428, "r": 545.11511, "b": 617.5108299999999, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "two consecutive decoders with different purposes. The first", "bbox": {"l": 308.86197, "t": 620.55928, "r": 545.11505, "b": 629.46584, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "decoder is the", "bbox": {"l": 308.86197, "t": 632.51428, "r": 364.78201, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "tag-decoder", "bbox": {"l": 367.57397, "t": 632.60394, "r": 415.61362, "b": 641.1917, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": ", i.e. it only produces the HTM-", "bbox": {"l": 415.61298, "t": 632.51428, "r": 545.11688, "b": 641.42084, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "L/LaTeX tags which construct an empty table. The second", "bbox": {"l": 308.86197, "t": 644.46928, "r": 545.11511, "b": 653.37584, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "content-decoder", "bbox": {"l": 308.86197, "t": 656.51494, "r": 373.59894, "b": 665.1027, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "uses the encoding of the image in combi-", "bbox": {"l": 376.90698, "t": 656.4252799999999, "r": 545.11548, "b": 665.33184, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "nation with the output encoding of each cell-tag (from the", "bbox": {"l": 308.862, "t": 668.38028, "r": 545.11517, "b": 677.28684, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "tag-decoder", "bbox": {"l": 308.862, "t": 680.42494, "r": 356.90164, "b": 689.0127, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": ") to generate the textual content of each table", "bbox": {"l": 357.13101, "t": 680.33528, "r": 545.1153, "b": 689.24184, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "cell. The network architecture of IEDD is certainly more", "bbox": {"l": 308.862, "t": 692.290283, "r": 545.11511, "b": 701.196846, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "elaborate, but it has the advantage that one can pre-train the", "bbox": {"l": 308.862, "t": 704.245285, "r": 545.11517, "b": 713.151848, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are \u201cimage-encoder \u2192 text-decoder\u201d (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the \u201cimage-encoder \u2192 dual decoder\u201d (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"id": 134, "text": "2", "bbox": {"l": 295.121, "t": 734.133282, "r": 300.10229, "b": 743.039845, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0", "bbox": {"l": 316.57742, "t": 152.47400000000005, "r": 320.3934, "b": 158.30602999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "7", "bbox": {"l": 312.19775, "t": 120.57050000000004, "r": 316.01376, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0", "bbox": {"l": 316.01196, "t": 120.57050000000004, "r": 319.82794, "b": 126.40252999999996, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "9", "bbox": {"l": 312.8165, "t": 90.1087, "r": 316.63251, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "0", "bbox": {"l": 316.63071, "t": 90.1087, "r": 320.44669, "b": 95.94073000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "0", "bbox": {"l": 532.17426, "t": 222.72729000000004, "r": 536.94427, "b": 230.01727000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "10K", "bbox": {"l": 532.87952, "t": 108.26702999999986, "r": 547.61249, "b": 115.55700999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "8K", "bbox": {"l": 532.7735, "t": 130.78101000000004, "r": 542.73877, "b": 138.07097999999996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "6K", "bbox": {"l": 532.79901, "t": 153.92352000000005, "r": 542.76428, "b": 161.21349999999995, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "4K", "bbox": {"l": 532.5705, "t": 176.75800000000004, "r": 542.53577, "b": 184.04796999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2K", "bbox": {"l": 532.14551, "t": 199.6463, "r": 542.11078, "b": 206.93628, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Figure 2:", "bbox": {"l": 308.862, "t": 267.83636, "r": 346.06238, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Distribution of the tables across different table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767216801643372, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878450036048889, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44086456298828, "r": 550.38916015625, "b": 250.60984802246094, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 233.42296999999996, "r": 508.47968000000003, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "10", "bbox": {"l": 365.29999, "t": 233.42296999999996, "r": 372.93018, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "30", "bbox": {"l": 455.66626, "t": 233.42296999999996, "r": 463.29645, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "50", "bbox": {"l": 542.03528, "t": 233.42296999999996, "r": 549.66547, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0", "bbox": {"l": 316.04474, "t": 230.44617000000005, "r": 319.86075, "b": 236.27819999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "2", "bbox": {"l": 312.62521, "t": 198.69073000000003, "r": 316.44122, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0", "bbox": {"l": 316.43942, "t": 198.69073000000003, "r": 320.2554, "b": 204.52277000000004, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "4", "bbox": {"l": 313.14951, "t": 168.09795999999994, "r": 316.96552, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "0", "bbox": {"l": 316.96371, "t": 168.09795999999994, "r": 320.77969, "b": 173.92998999999998, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "6", "bbox": {"l": 312.92972, "t": 136.58771000000002, "r": 316.74573, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "0", "bbox": {"l": 316.74393, "t": 136.58771000000002, "r": 320.55991, "b": 142.41974000000005, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "8", "bbox": {"l": 312.48227, "t": 105.60175000000004, "r": 316.29828, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0", "bbox": {"l": 316.29648, "t": 105.60175000000004, "r": 320.11246, "b": 111.43377999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "1", "bbox": {"l": 312.48227, "t": 212.25922000000003, "r": 316.29828, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0", "bbox": {"l": 316.29648, "t": 212.25922000000003, "r": 320.11246, "b": 218.09124999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "3", "bbox": {"l": 313.07639, "t": 183.72198000000003, "r": 316.8924, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "0", "bbox": {"l": 316.89059, "t": 183.72198000000003, "r": 320.70657, "b": 189.55402000000004, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "5", "bbox": {"l": 312.76321, "t": 152.47400000000005, "r": 316.57922, "b": 158.30602999999996, 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table", "bbox": {"l": 354.49072, "t": 267.83636, "r": 545.11511, "b": 276.74292, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "dimensions in PubTabNet + FinTabNet datasets", "bbox": {"l": 308.862, "t": 279.79132000000004, "r": 498.56989, "b": 288.6979099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879811763763428, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767216801643372, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878450036048889, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. These datasets span over various ap-", "bbox": {"l": 50.111984, "t": 680.3353, "r": 286.36502, "b": 689.24186, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "pearance styles and content.", "bbox": {"l": 50.111984, "t": 692.290298, "r": 166.24602, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "We also introduce our own", "bbox": {"l": 173.68808, "t": 692.290298, "r": 286.36508, "b": 701.196861, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "synthetically generated SynthTabNet dataset to fix an im-", "bbox": {"l": 50.111984, "t": 704.2453, "r": 286.36505, "b": 713.151863, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-"}, {"label": "picture", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 312.10369873046875, "t": 78.44086456298828, "r": 550.38916015625, "b": 250.60984802246094, "coord_origin": "TOPLEFT"}, "confidence": 0.9746918082237244, "cells": [{"id": 65, "text": "PubTabNet + FinTabNet", "bbox": {"l": 380.79849, "t": 79.81176999999991, "r": 486.84909, "b": 88.55975000000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Rows / Columns", "bbox": {"l": 396.76776, "t": 242.02697999999998, "r": 469.78748, "b": 250.77495999999996, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0", "bbox": {"l": 320.97653, "t": 233.42296999999996, "r": 324.79254, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "20", "bbox": {"l": 410.483, "t": 233.42296999999996, "r": 418.11319, "b": 239.255, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "40", "bbox": {"l": 500.84949, "t": 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The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8767216801643372, "cells": [{"id": 0, "text": "tag-decoder which is constrained to the table-tags.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 250.15102, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tag-decoder which is constrained to the table-tags."}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 87.21935999999994, "r": 286.36514, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9822593927383423, "cells": [{"id": 1, "text": "In", "bbox": {"l": 62.067001, "t": 87.21935999999994, "r": 70.365845, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "practice,", "bbox": {"l": 76.931198, "t": 87.21935999999994, "r": 110.95348000000001, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "both", "bbox": {"l": 118.54498, "t": 87.21935999999994, "r": 136.25848, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "network", "bbox": {"l": 142.82384, "t": 87.21935999999994, "r": 175.37166, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "architectures", "bbox": {"l": 181.94698, "t": 87.21935999999994, "r": 232.83594000000002, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(IETD", "bbox": {"l": 239.41125, "t": 87.21935999999994, "r": 265.41364, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "and", "bbox": {"l": 271.979, "t": 87.21935999999994, "r": 286.36499, "b": 96.12591999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "IEDD) require an implicit, custom trained object-character-", "bbox": {"l": 50.112, "t": 99.17437999999993, "r": 286.36505, "b": 108.08092999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "recognition (OCR) to obtain the content of the table-cells.", "bbox": {"l": 50.112, "t": 111.13036999999997, "r": 286.36511, "b": 120.03692999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "In the case of IETD, this OCR engine is implicit in the de-", "bbox": {"l": 50.112, "t": 123.08538999999996, "r": 286.36505, "b": 131.99194, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "bbox": {"l": 50.112, "t": 135.04040999999995, "r": 286.36514, "b": 143.94696, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bedded in the content-decoder. This reliance on a custom,", "bbox": {"l": 50.112, "t": 146.99541999999997, "r": 286.36511, "b": 155.90197999999998, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "implicit OCR decoder is of course problematic. OCR is a", "bbox": {"l": 50.112, "t": 158.95043999999996, "r": 286.36505, "b": 167.85699, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "well known and extremely tough problem, that often needs", "bbox": {"l": 50.112, "t": 170.90545999999995, "r": 286.36508, "b": 179.81201, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "custom training for each individual language. However, the", "bbox": {"l": 50.112, "t": 182.86145, "r": 286.36508, "b": 191.76801, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "limited availability for non-english content in the current", "bbox": {"l": 50.112, "t": 194.81646999999998, "r": 286.36511, "b": 203.72302000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "datasets, makes it impractical to apply the IETD and IEDD", "bbox": {"l": 50.112, "t": 206.77148, "r": 286.36511, "b": 215.67804, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "methods on tables with other languages. Additionally, OCR", "bbox": {"l": 50.112, "t": 218.7265, "r": 286.36505, "b": 227.63306, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can be completely omitted if the tables originate from pro-", "bbox": {"l": 50.112, "t": 230.68151999999998, "r": 286.36505, "b": 239.58807000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "grammatic PDF documents with known positions of each", "bbox": {"l": 50.112, "t": 242.63653999999997, "r": 286.36511, "b": 251.54309, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cell. The latter was the inspiration for the work of this pa-", "bbox": {"l": 50.112, "t": 254.59253, "r": 286.36508, "b": 263.49908000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "per.", "bbox": {"l": 50.112, "t": 266.54755, "r": 64.776947, "b": 275.45410000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111992, "t": 278.43895999999995, "r": 286.36511, "b": 490.70288, "coord_origin": "TOPLEFT"}, "confidence": 0.9878450036048889, "cells": [{"id": 23, "text": "Graph Neural networks", "bbox": {"l": 62.067001, "t": 278.43895999999995, "r": 171.56593, "b": 287.39536, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ":", "bbox": {"l": 171.56799, "t": 278.55853, "r": 174.3376, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Graph Neural networks", "bbox": {"l": 185.18687, "t": 278.55853, "r": 286.35709, "b": 287.46509, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "(GNN\u2019s) take a radically different approach to table-", "bbox": {"l": 50.111992, "t": 290.51453000000004, "r": 286.36511, "b": 299.42108, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "structure extraction.", "bbox": {"l": 50.111992, "t": 302.46950999999996, "r": 131.16771, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Note that one table cell can consti-", "bbox": {"l": 138.84888, "t": 302.46950999999996, "r": 286.36508, "b": 311.37607, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "tute out of multiple text-cells. To obtain the table-structure,", "bbox": {"l": 50.111992, "t": 314.4245, "r": 286.36505, "b": 323.33105, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "one creates an initial graph, where each of the text-cells", "bbox": {"l": 50.111992, "t": 326.37949000000003, "r": 286.36508, "b": 335.28604, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "becomes a node in the graph similar to [33, 34, 2]. Each", "bbox": {"l": 50.111992, "t": 338.33447, "r": 286.36505, "b": 347.2410300000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "node is then associated with en embedding vector coming", "bbox": {"l": 50.111992, "t": 350.28946, "r": 286.36505, "b": 359.19601, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "from the encoded image, its coordinates and the encoded", "bbox": {"l": 50.111992, "t": 362.24545000000006, "r": 286.36508, "b": 371.15201, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "text. Furthermore, nodes that represent adjacent text-cells", "bbox": {"l": 50.111992, "t": 374.20044, "r": 286.36508, "b": 383.10699, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "are linked. Graph Convolutional Networks (GCN\u2019s) based", "bbox": {"l": 50.111992, "t": 386.15542999999997, "r": 286.36508, "b": 395.06198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "methods take the image as an input, but also the position of", "bbox": {"l": 50.111992, "t": 398.11041000000006, "r": 286.36508, "b": 407.01697, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the text-cells and their content [18]. The purpose of a GCN", "bbox": {"l": 50.111992, "t": 410.0654, "r": 286.36508, "b": 418.97195, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "is to transform the input graph into a new graph, which re-", "bbox": {"l": 50.111992, "t": 422.02038999999996, "r": 286.36505, "b": 430.92694, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "places the old links with new ones.", "bbox": {"l": 50.111992, "t": 433.97638, "r": 198.2359, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "The new links then", "bbox": {"l": 205.92703, "t": 433.97638, "r": 286.36505, "b": 442.88293, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "represent the table-structure. With this approach, one can", "bbox": {"l": 50.111992, "t": 445.93137, "r": 286.36508, "b": 454.83792000000005, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "avoid the need to build custom OCR decoders. However,", "bbox": {"l": 50.111992, "t": 457.88635, "r": 286.36505, "b": 466.79291, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the quality of the reconstructed structure is not comparable", "bbox": {"l": 50.111992, "t": 469.84134, "r": 286.36505, "b": 478.74789, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "to the current state-of-the-art [18].", "bbox": {"l": 50.111992, "t": 481.79633, "r": 186.49998, "b": 490.70288, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN\u2019s) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN\u2019s) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.111984, "t": 493.68875, "r": 286.36627, "b": 622.26685, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"id": 45, "text": "Hybrid Deep Learning-Rule-Based approach", "bbox": {"l": 62.066994, "t": 493.68875, "r": 252.88068000000004, "b": 502.64514, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": ": A pop-", "bbox": {"l": 252.88199, "t": 493.80832, "r": 286.36627, "b": 502.71487, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "ular current model for table-structure identification is the", "bbox": {"l": 50.111984, "t": 505.76331, "r": 286.36505, "b": 514.66986, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "bbox": {"l": 50.111984, "t": 517.71829, "r": 286.36505, "b": 526.6248499999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "to [27, 29]. In this approach, one first detects the position of", "bbox": {"l": 50.111984, "t": 529.67328, "r": 286.36508, "b": 538.57985, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "bbox": {"l": 50.111984, "t": 541.62929, "r": 286.36508, "b": 550.53584, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "RCNN), then classifies the table into different types (from", "bbox": {"l": 50.111984, "t": 553.58429, "r": 286.36511, "b": 562.4908399999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "its images) and finally uses different rule-sets to obtain", "bbox": {"l": 50.111984, "t": 565.5392899999999, "r": 286.36511, "b": 574.44585, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "its table-structure. Currently, this approach achieves state-", "bbox": {"l": 50.111984, "t": 577.49429, "r": 286.36502, "b": 586.40085, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "of-the-art results, but is not an end-to-end deep-learning", "bbox": {"l": 50.111984, "t": 589.4493, "r": 286.36505, "b": 598.35585, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "method. As such, new rules need to be written if different", "bbox": {"l": 50.111984, "t": 601.4043, "r": 286.36502, "b": 610.31085, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "types of tables are encountered.", "bbox": {"l": 50.111984, "t": 613.36029, "r": 175.98943, "b": 622.26685, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.111984, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"id": 57, "text": "3.", "bbox": {"l": 50.111984, "t": 635.94484, "r": 57.82375699999999, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Datasets", "bbox": {"l": 68.106125, "t": 635.94484, "r": 105.22546, "b": 646.6925699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 656.42529, "r": 286.36508, "b": 713.151863, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"id": 59, "text": "We rely on large-scale datasets such as PubTabNet [37],", "bbox": {"l": 62.06698600000001, "t": 656.42529, "r": 286.36493, "b": 665.33186, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "bbox": {"l": 50.111984, "t": 668.38029, "r": 286.36508, "b": 677.2868599999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "evaluate our models. 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the tables across different table dimensions in PubTabNet + FinTabNet datasets"}, {"label": "text", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}, "confidence": 0.8879811763763428, "cells": [{"id": 101, "text": "balance in the previous datasets.", "bbox": {"l": 308.862, "t": 317.47336, "r": 437.27002, "b": 326.37991, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.53137, "r": 545.11517, "b": 627.36174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"id": 102, "text": "The PubTabNet dataset contains 509k tables delivered as", "bbox": {"l": 320.81699, "t": 331.53137, "r": 545.11505, "b": 340.43793, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "annotated PNG images. The annotations consist of the table", "bbox": {"l": 308.862, "t": 343.48635999999993, "r": 545.11517, "b": 352.39291, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "structure represented in HTML format, the tokenized text", "bbox": {"l": 308.862, "t": 355.44235, "r": 545.11505, "b": 364.34890999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "bbox": {"l": 308.862, "t": 367.39734, "r": 545.11505, "b": 376.30389, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "pearance style of PubTabNet. Depending on its complexity,", "bbox": {"l": 308.862, "t": 379.35233, "r": 545.11511, "b": 388.25888, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "a table is characterized as \u201csimple\u201d when it does not contain", "bbox": {"l": 308.862, "t": 391.30731, "r": 545.11511, "b": 400.21386999999993, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "row spans or column spans, otherwise it is \u201ccomplex\u201d. The", "bbox": {"l": 308.862, "t": 403.26230000000004, "r": 545.11505, "b": 412.16885, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dataset is divided into Train and Val splits (roughly 98% and", "bbox": {"l": 308.862, "t": 415.21729, "r": 545.11511, "b": 424.12384, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "2%). The Train split consists of 54% simple and 46% com-", "bbox": {"l": 308.862, "t": 427.17328, "r": 545.11517, "b": 436.0798300000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "plex tables and the Val split of 51% and 49% respectively.", "bbox": {"l": 308.862, "t": 439.12827, "r": 545.11517, "b": 448.03482, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "The FinTabNet dataset contains 112k tables delivered as", "bbox": {"l": 308.862, "t": 451.08325, "r": 545.11511, "b": 459.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "single-page PDF documents with mixed table structures and", "bbox": {"l": 308.862, "t": 463.03824, "r": 545.11505, "b": 471.94479, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "text content. Similarly to the PubTabNet, the annotations", "bbox": {"l": 308.862, "t": 474.99323, "r": 545.11511, "b": 483.89978, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "of FinTabNet include the table structure in HTML, the to-", "bbox": {"l": 308.862, "t": 486.94922, "r": 545.11511, "b": 495.85577, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "kenized text and the bounding boxes on a table cell basis.", "bbox": {"l": 308.862, "t": 498.90421, "r": 545.11511, "b": 507.81076, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "The dataset is divided into Train, Test and Val splits (81%,", "bbox": {"l": 308.862, "t": 510.85919, "r": 545.11517, "b": 519.76575, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "9.5%, 9.5%), and each one is almost equally divided into", "bbox": {"l": 308.862, "t": 522.8141800000001, "r": 545.11517, "b": 531.72073, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "simple and complex tables (Train: 48% simple, 52% com-", "bbox": {"l": 308.862, "t": 534.76917, "r": 545.11505, "b": 543.67574, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "plex, Test: 48% simple, 52% complex, Test: 53% simple,", "bbox": {"l": 308.862, "t": 546.72418, "r": 545.11511, "b": 555.6307400000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "47% complex). Finally the TableBank dataset consists of", "bbox": {"l": 308.862, "t": 558.6801800000001, "r": 545.11511, "b": 567.58673, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "145k tables provided as JPEG images. The latter has anno-", "bbox": {"l": 308.862, "t": 570.63518, "r": 545.11505, "b": 579.54173, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "tations for the table structure, but only few with bounding", "bbox": {"l": 308.862, "t": 582.59018, "r": 545.11499, "b": 591.49673, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "boxes of the table cells. The entire dataset consists of sim-", "bbox": {"l": 308.862, "t": 594.54518, "r": 545.11517, "b": 603.45174, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "bbox": {"l": 308.862, "t": 606.50018, "r": 545.11511, "b": 615.40674, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Val splits.", "bbox": {"l": 308.862, "t": 618.45518, "r": 348.16446, "b": 627.36174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as \u201csimple\u201d when it does not contain row spans or column spans, otherwise it is \u201ccomplex\u201d. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.51419, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"id": 127, "text": "Due to the heterogeneity across the dataset formats, it", "bbox": {"l": 320.81699, "t": 632.51419, "r": 545.11487, "b": 641.42075, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "was necessary to combine all available data into one homog-", "bbox": {"l": 308.862, "t": 644.46919, "r": 545.11511, "b": 653.37575, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "enized dataset before we could train our models for practi-", "bbox": {"l": 308.862, "t": 656.42419, "r": 545.11511, "b": 665.33076, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "cal purposes. Given the size of PubTabNet, we adopted its", "bbox": {"l": 308.862, "t": 668.38019, "r": 545.11499, "b": 677.28676, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "annotation format and we extracted and converted all tables", "bbox": {"l": 308.862, "t": 680.33519, "r": 545.11505, "b": 689.24176, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "as PNG images with a resolution of 72 dpi. Additionally,", "bbox": {"l": 308.862, "t": 692.290192, "r": 545.11505, "b": 701.196762, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "we have filtered out tables with extreme sizes due to small", "bbox": {"l": 308.862, "t": 704.245193, "r": 545.11511, "b": 713.151764, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"id": 134, "text": "3", "bbox": {"l": 295.121, "t": 734.133198, "r": 300.10229, "b": 743.039761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Combined-Tabnet(*)", "bbox": {"l": 317.06, "t": 121.83336999999995, "r": 400.37723, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3", "bbox": {"l": 417.85599, "t": 121.53448000000003, "r": 425.37775, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3", "bbox": {"l": 449.89569, "t": 121.53448000000003, "r": 457.41745000000003, "b": 130.74987999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "400k", "bbox": {"l": 476.401, "t": 121.83336999999995, "r": 496.3262, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "PNG", "bbox": {"l": 512.63495, "t": 121.83336999999995, "r": 532.56012, "b": 130.73992999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Combined(**)", "bbox": {"l": 317.06, "t": 133.78839000000005, "r": 375.17184, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "3", "bbox": {"l": 417.85599, "t": 133.48950000000002, "r": 425.37775, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3", "bbox": {"l": 449.89569, "t": 133.48950000000002, "r": 457.41745000000003, "b": 142.70489999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "500k", "bbox": {"l": 476.401, "t": 133.78839000000005, "r": 496.3262, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "PNG", "bbox": {"l": 512.63495, "t": 133.78839000000005, "r": 532.56012, "b": 142.69494999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "SynthTabNet", "bbox": {"l": 317.06, "t": 145.74341000000004, "r": 369.39352, "b": 154.64995999999996, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "3", "bbox": {"l": 417.85599, "t": 145.44446000000005, "r": 425.37775, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3", "bbox": {"l": 449.89569, "t": 145.44446000000005, "r": 457.41745000000003, "b": 154.65985, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "600k", "bbox": {"l": 476.401, "t": 145.74334999999996, "r": 496.3262, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "PNG", "bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19388580322266, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 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While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565718531608582, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {"8": {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19388580322266, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": 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"children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19388580322266, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "112k", "bbox": {"l": 476.401, "t": 97.92236000000003, "r": 496.3262, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "PDF", "bbox": {"l": 513.46185, "t": 97.92236000000003, "r": 531.73328, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "TableBank", "bbox": {"l": 317.06, "t": 109.87836000000004, "r": 359.97888, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "3", "bbox": {"l": 417.85599, "t": 109.57947000000001, "r": 425.37775, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "7", "bbox": {"l": 450.81226, "t": 109.57947000000001, "r": 456.50091999999995, "b": 118.79485999999997, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "145k", "bbox": {"l": 476.401, "t": 109.87836000000004, "r": 496.3262, "b": 118.78490999999997, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "JPEG", "bbox": {"l": 511.25017999999994, "t": 109.87836000000004, "r": 533.94501, "b": 118.78490999999997, 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"b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "600k", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 512.63495, "t": 145.74334999999996, "r": 532.56012, "b": 154.6499, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "PNG", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.66138, "r": 545.11505, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9667726755142212, "cells": [{"id": 91, "text": "Table 1:", "bbox": {"l": 308.862, "t": 167.66138, "r": 344.6178, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Both", "bbox": {"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565718531608582, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"id": 0, "text": "amount of such tables, and kept only those ones ranging", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36511, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "between 1*1 and 20*10 (rows/columns).", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 212.28319, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 100.96038999999996, "r": 286.36514, "b": 313.10507, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"id": 2, "text": "The availability of the bounding boxes for all table cells", "bbox": {"l": 62.067001, "t": 100.96038999999996, "r": 286.36502, "b": 109.86694, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "is essential to train our models. In order to distinguish be-", "bbox": {"l": 50.112, "t": 112.91540999999995, "r": 286.36508, "b": 121.82195999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "tween empty and non-empty bounding boxes, we have in-", "bbox": {"l": 50.112, "t": 124.87041999999997, "r": 286.36508, "b": 133.77697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "troduced a binary class in the annotation. Unfortunately, the", "bbox": {"l": 50.112, "t": 136.82641999999998, "r": 286.36511, "b": 145.73297000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "original datasets either omit the bounding boxes for whole", "bbox": {"l": 50.112, "t": 148.78143, "r": 286.36511, "b": 157.68799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "tables (e.g. TableBank) or they narrow their scope only to", "bbox": {"l": 50.112, "t": 160.73645, "r": 286.36508, "b": 169.64301, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "non-empty cells. Therefore, it was imperative to introduce", "bbox": {"l": 50.112, "t": 172.69146999999998, "r": 286.36505, "b": 181.59802000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "a data pre-processing procedure that generates the missing", "bbox": {"l": 50.112, "t": 184.64648, "r": 286.36508, "b": 193.55304, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "bounding boxes out of the annotation information. This pro-", "bbox": {"l": 50.112, "t": 196.60248, "r": 286.36508, "b": 205.50903000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "cedure first parses the provided table structure and calcu-", "bbox": {"l": 50.112, "t": 208.5575, "r": 286.36508, "b": 217.46405000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "lates the dimensions of the most fine-grained grid that cov-", "bbox": {"l": 50.112, "t": 220.51251000000002, "r": 286.36511, "b": 229.41907000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ers the table structure. Notice that each table cell may oc-", "bbox": {"l": 50.112, "t": 232.46753, "r": 286.36508, "b": 241.37408000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "cupy multiple grid squares due to row or column spans. In", "bbox": {"l": 50.112, "t": 244.42255, "r": 286.36508, "b": 253.32910000000004, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "case of PubTabNet we had to compute missing bounding", "bbox": {"l": 50.112, "t": 256.37756, "r": 286.36505, "b": 265.28412000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "boxes for 48% of the simple and 69% of the complex ta-", "bbox": {"l": 50.112, "t": 268.33356000000003, "r": 286.36505, "b": 277.24010999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "bles.", "bbox": {"l": 50.112, "t": 280.28853999999995, "r": 68.652397, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Regarding FinTabNet, 68% of the simple and 98%", "bbox": {"l": 75.566444, "t": 280.28853999999995, "r": 286.36514, "b": 289.1951, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of the complex tables require the generation of bounding", "bbox": {"l": 50.112, "t": 292.24353, "r": 286.36511, "b": 301.15009, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "boxes.", "bbox": {"l": 50.112, "t": 304.19852000000003, "r": 75.695961, "b": 313.10507, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 317.99550999999997, "r": 286.36511, "b": 434.49896, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"id": 21, "text": "As it is illustrated in Fig. 2, the table distributions from", "bbox": {"l": 62.067001, "t": 317.99550999999997, "r": 286.36499, "b": 326.90207, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "all datasets are skewed towards simpler structures with", "bbox": {"l": 50.112, "t": 329.95151, "r": 286.36511, "b": 338.8580600000001, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "fewer number of rows/columns. Additionally, there is very", "bbox": {"l": 50.112, "t": 341.90649, "r": 286.36502, "b": 350.81305, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "limited variance in the table styles, which in case of Pub-", "bbox": {"l": 50.112, "t": 353.8614799999999, "r": 286.36505, "b": 362.76804, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "TabNet and FinTabNet means one styling format for the", "bbox": {"l": 50.112, "t": 365.81647, "r": 286.36508, "b": 374.72301999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "majority of the tables.", "bbox": {"l": 50.112, "t": 377.77145, "r": 141.58859, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Similar limitations appear also in", "bbox": {"l": 148.70189, "t": 377.77145, "r": 286.36508, "b": 386.67801, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "the type of table content, which in some cases (e.g. FinTab-", "bbox": {"l": 50.112, "t": 389.72644, "r": 286.36508, "b": 398.63300000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Net) is restricted to a certain domain. Ultimately, the lack", "bbox": {"l": 50.112, "t": 401.68243, "r": 286.36511, "b": 410.58899, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "of diversity in the training dataset damages the ability of the", "bbox": {"l": 50.112, "t": 413.63742, "r": 286.36511, "b": 422.54398, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "models to generalize well on unseen data.", "bbox": {"l": 50.112, "t": 425.59241, "r": 216.39774, "b": 434.49896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.3894, "r": 286.36655, "b": 627.62389, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"id": 32, "text": "Motivated by those observations we aimed at generating", "bbox": {"l": 62.067001, "t": 439.3894, "r": 286.36499, "b": 448.2959599999999, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "a synthetic table dataset named", "bbox": {"l": 50.112, "t": 451.34439, "r": 172.14388, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "SynthTabNet", "bbox": {"l": 174.14801, "t": 451.43405, "r": 224.70818999999997, "b": 460.02182, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ". This approach", "bbox": {"l": 224.70801, "t": 451.34439, "r": 286.36655, "b": 460.25095, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "offers control over: 1) the size of the dataset, 2) the table", "bbox": {"l": 50.112015, "t": 463.30038, "r": 286.36505, "b": 472.20694, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "structure, 3) the table style and 4) the type of content. The", "bbox": {"l": 50.112015, "t": 475.25537, "r": 286.36511, "b": 484.16193, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "complexity of the table structure is described by the size of", "bbox": {"l": 50.112015, "t": 487.21036, "r": 286.36511, "b": 496.11691, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "the table header and the table body, as well as the percentage", "bbox": {"l": 50.112015, "t": 499.16534, "r": 286.36508, "b": 508.0719, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "of the table cells covered by row spans and column spans.", "bbox": {"l": 50.112015, "t": 511.12033, "r": 286.36505, "b": 520.02689, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "A set of carefully designed styling templates provides the", "bbox": {"l": 50.112015, "t": 523.07632, "r": 286.36508, "b": 531.98288, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "basis to build a wide range of table appearances. Lastly, the", "bbox": {"l": 50.112015, "t": 535.0313100000001, "r": 286.36508, "b": 543.93788, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "table content is generated out of a curated collection of text", "bbox": {"l": 50.112015, "t": 546.98633, "r": 286.36511, "b": 555.89288, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "corpora. By controlling the size and scope of the synthetic", "bbox": {"l": 50.112015, "t": 558.94133, "r": 286.36508, "b": 567.84789, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "datasets we are able to train and evaluate our models in a", "bbox": {"l": 50.112015, "t": 570.89633, "r": 286.36511, "b": 579.8028899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "variety of different conditions. For example, we can first", "bbox": {"l": 50.112015, "t": 582.85133, "r": 286.36511, "b": 591.75789, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "generate a highly diverse dataset to train our models and", "bbox": {"l": 50.112015, "t": 594.80733, "r": 286.36505, "b": 603.71388, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "then evaluate their performance on other synthetic datasets", "bbox": {"l": 50.112015, "t": 606.76233, "r": 286.36508, "b": 615.6688800000001, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "which are focused on a specific domain.", "bbox": {"l": 50.112015, "t": 618.71733, "r": 209.7527, "b": 627.62389, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112015, "t": 632.51433, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"id": 50, "text": "In this regard, we have prepared four synthetic datasets,", "bbox": {"l": 62.067017, "t": 632.51433, "r": 286.36499, "b": 641.42088, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "each one containing 150k examples. The corpora to gener-", "bbox": {"l": 50.112015, "t": 644.46933, "r": 286.36508, "b": 653.37589, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ate the table text consists of the most frequent terms appear-", "bbox": {"l": 50.112015, "t": 656.42532, "r": 286.36511, "b": 665.33189, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ing in PubTabNet and FinTabNet together with randomly", "bbox": {"l": 50.112015, "t": 668.38033, "r": 286.36505, "b": 677.28689, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "generated text. The first two synthetic datasets have been", "bbox": {"l": 50.112015, "t": 680.33533, "r": 286.36508, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "fine-tuned to mimic the appearance of the original datasets", "bbox": {"l": 50.112015, "t": 692.290329, "r": 286.36508, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "but encompass more complicated table structures. The third", "bbox": {"l": 50.112015, "t": 704.245331, "r": 286.36511, "b": 713.151894, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third"}, {"label": "table", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "table", "bbox": {"l": 310.67584228515625, "t": 73.19388580322266, "r": 542.9547119140625, "b": 155.22052001953125, "coord_origin": "TOPLEFT"}, "confidence": 0.9777463674545288, "cells": [{"id": 57, "text": "Tags", "bbox": {"l": 412.332, "t": 73.61437999999998, "r": 430.90231, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Bbox", "bbox": {"l": 442.85742, "t": 73.61437999999998, "r": 464.4463799999999, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Size", "bbox": {"l": 477.78632, "t": 73.61437999999998, "r": 494.94193, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Format", "bbox": {"l": 508.28186, "t": 73.61437999999998, "r": 536.91437, "b": 82.52094, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "PubTabNet", "bbox": {"l": 317.06, "t": 85.9673499999999, "r": 361.64264, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3", "bbox": {"l": 417.85599, "t": 85.6684600000001, "r": 425.37775, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "3", "bbox": {"l": 449.89569, "t": 85.6684600000001, "r": 457.41745000000003, "b": 94.88385000000017, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "509k", "bbox": {"l": 476.401, "t": 85.9673499999999, "r": 496.3262, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PNG", "bbox": {"l": 512.63495, "t": 85.9673499999999, "r": 532.56012, "b": 94.87390000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "FinTabNet", "bbox": {"l": 317.06, "t": 97.92236000000003, "r": 359.43094, "b": 106.82892000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "3", "bbox": {"l": 417.85599, "t": 97.62347, "r": 425.37775, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 449.89569, "t": 97.62347, "r": 457.41745000000003, "b": 106.83887000000016, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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{"l": 361.07602, "t": 167.66138, "r": 380.45328, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "\u201cCombined-Tabnet\u201d", "bbox": {"l": 386.56799, "t": 167.75104, "r": 468.67974999999996, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and", "bbox": {"l": 474.79599, "t": 167.66138, "r": 489.18198, "b": 176.56793000000005, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "\u201dCombined-", "bbox": {"l": 495.29898000000003, "t": 167.75104, "r": 545.112, "b": 176.33880999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Tabnet\u201d", "bbox": {"l": 308.862, "t": 179.70605, "r": 341.16077, "b": 188.29381999999998, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "are variations of the following: (*) The Combined-", "bbox": {"l": 343.457, "t": 179.61639000000002, "r": 545.11005, "b": 188.52295000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Tabnet dataset is the processed combination of PubTabNet", "bbox": {"l": 308.862, "t": 191.57141000000001, "r": 545.11505, "b": 200.47797000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "and Fintabnet. (**) The combined dataset is the processed", "bbox": {"l": 308.862, "t": 203.52643, "r": 545.11499, "b": 212.43298000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "combination of PubTabNet, Fintabnet and TableBank.", "bbox": {"l": 308.862, "t": 215.48242000000005, "r": 523.93469, "b": 224.38897999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: Both \u201cCombined-Tabnet\u201d and \u201dCombinedTabnet\u201d are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 294.39197, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"id": 101, "text": "one adopts a colorful appearance with high contrast and the", "bbox": {"l": 308.862, "t": 249.62041999999997, "r": 545.11517, "b": 258.52698, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "last one contains tables with sparse content. Lastly, we have", "bbox": {"l": 308.862, "t": 261.57543999999996, "r": 545.11517, "b": 270.48199, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "combined all synthetic datasets into one big unified syn-", "bbox": {"l": 308.862, "t": 273.5304, "r": 545.11505, "b": 282.43698, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "thetic dataset of 600k examples.", "bbox": {"l": 308.862, "t": 285.48541000000006, "r": 436.82169, "b": 294.39197, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"id": 105, "text": "Tab. 1 summarizes the various attributes of the datasets.", "bbox": {"l": 320.81699, "t": 297.77240000000006, "r": 542.74396, "b": 306.67896, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"id": 106, "text": "4.", "bbox": {"l": 308.862, "t": 321.18396, "r": 316.28476, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "The TableFormer model", "bbox": {"l": 326.18176, "t": 321.18396, "r": 444.93607000000003, "b": 331.93167000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 341.93939, "r": 545.11523, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"id": 108, "text": "Given the image of a table, TableFormer is able to pre-", "bbox": {"l": 320.81699, "t": 341.93939, "r": 545.11499, "b": 350.84594999999996, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "dict: 1) a sequence of tokens that represent the structure of", "bbox": {"l": 308.862, "t": 353.89438, "r": 545.11511, "b": 362.80092999999994, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "a table, and 2) a bounding box coupled to a subset of those", "bbox": {"l": 308.862, "t": 365.84937, "r": 545.11517, "b": 374.75592, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "tokens. The conversion of an image into a sequence of to-", "bbox": {"l": 308.862, "t": 377.80435, "r": 545.11505, "b": 386.71091, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "kens is a well-known task [35, 16]. While attention is often", "bbox": {"l": 308.862, "t": 389.75934000000007, "r": 545.11517, "b": 398.66588999999993, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "used as an implicit method to associate each token of the", "bbox": {"l": 308.862, "t": 401.71432000000004, "r": 545.11523, "b": 410.62088, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "sequence with a position in the original image, an explicit", "bbox": {"l": 308.862, "t": 413.67032, "r": 545.11517, "b": 422.57687, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "association between the individual table-cells and the image", "bbox": {"l": 308.862, "t": 425.62531, "r": 545.11505, "b": 434.53186, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "bounding boxes is also required.", "bbox": {"l": 308.862, "t": 437.58029, "r": 437.9375, "b": 446.48685000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"id": 117, "text": "4.1.", "bbox": {"l": 308.862, "t": 457.69427, "r": 323.14081, "b": 467.54633, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Model architecture.", "bbox": {"l": 332.66003, "t": 457.69427, "r": 420.16058, "b": 467.54633, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86197, "t": 476.76529, "r": 545.11572, "b": 664.99981, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"id": 119, "text": "We now describe in detail the proposed method, which", "bbox": {"l": 320.81699, "t": 476.76529, "r": 545.11487, "b": 485.67184, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "is composed of three main components, see Fig.", "bbox": {"l": 308.862, "t": 488.72028, "r": 509.02054, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "4.", "bbox": {"l": 515.58588, "t": 488.72028, "r": 523.05786, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Our", "bbox": {"l": 529.62323, "t": 488.72028, "r": 545.11505, "b": 497.62683, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "CNN Backbone Network", "bbox": {"l": 308.862, "t": 500.76492, "r": 406.34601, "b": 509.35269, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "encodes the input as a feature vec-", "bbox": {"l": 408.87201, "t": 500.67526, "r": 545.1106, "b": 509.58182, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "tor of predefined length.", "bbox": {"l": 308.862, "t": 512.63126, "r": 409.39459, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "The input feature vector of the", "bbox": {"l": 416.72705, "t": 512.63126, "r": 545.11505, "b": 521.53781, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "encoded image is passed to the", "bbox": {"l": 308.862, "t": 524.58624, "r": 436.194, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Structure Decoder", "bbox": {"l": 439.526, "t": 524.6759, "r": 513.86694, "b": 533.26367, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "to pro-", "bbox": {"l": 517.43201, "t": 524.58624, "r": 545.10815, "b": 533.4928, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "duce a sequence of HTML tags that represent the structure", "bbox": {"l": 308.862, "t": 536.54124, "r": 545.11511, "b": 545.4478, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "of the table.", "bbox": {"l": 308.862, "t": 548.49625, "r": 358.5455, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "With each prediction of an HTML standard", "bbox": {"l": 365.19055, "t": 548.49625, "r": 545.11517, "b": 557.4028000000001, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "data cell (\u2018", "bbox": {"l": 308.862, "t": 560.45125, "r": 352.40851, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "<", "bbox": {"l": 352.409, "t": 560.29184, "r": 360.1579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "td", "bbox": {"l": 360.15799, "t": 560.45125, "r": 367.90891, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ">", "bbox": {"l": 367.909, "t": 560.29184, "r": 375.6579, "b": 569.13863, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "\u2019) the hidden state of that cell is passed to", "bbox": {"l": 375.65799, "t": 560.45125, "r": 545.11182, "b": 569.3578, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "bbox": {"l": 308.862, "t": 572.40724, "r": 545.11499, "b": 581.3138, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "or column span, the tag is broken down to \u2018", "bbox": {"l": 308.862, "t": 584.3622399999999, "r": 483.11768, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "<", "bbox": {"l": 483.11902, "t": 584.20284, "r": 490.86792, "b": 593.04962, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "\u2019, \u2018rowspan=\u2019", "bbox": {"l": 490.86800999999997, "t": 584.3622399999999, "r": 545.11438, "b": 593.2688, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "or \u2018colspan=\u2019, with the number of spanning cells (attribute),", "bbox": {"l": 308.862, "t": 596.31725, "r": 545.11493, "b": 605.2238, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "and \u2018", "bbox": {"l": 308.862, "t": 608.27225, "r": 329.64395, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ">", "bbox": {"l": 329.646, "t": 608.11284, "r": 337.3949, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "\u2019. The hidden state attached to \u2018", "bbox": {"l": 337.39398, "t": 608.27225, "r": 468.5914, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "<", "bbox": {"l": 468.59496999999993, "t": 608.11284, "r": 476.34387000000004, "b": 616.9596300000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "\u2019 is passed to the", "bbox": {"l": 476.3439599999999, "t": 608.27225, "r": 545.11572, "b": 617.1788, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "bbox": {"l": 308.86197, "t": 620.22725, "r": 545.11499, "b": 629.1338000000001, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "receives the hidden states from the Structure Decoder, to", "bbox": {"l": 308.86197, "t": 632.1822500000001, "r": 545.11517, "b": 641.08881, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "provide the final detection predictions of the bounding box", "bbox": {"l": 308.86197, "t": 644.13824, "r": 545.11511, "b": 653.0448, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "coordinates and their classification.", "bbox": {"l": 308.86197, "t": 656.09325, "r": 449.42432, "b": 664.99981, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (\u2018 < td > \u2019) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to \u2018 < \u2019, \u2018rowspan=\u2019 or \u2018colspan=\u2019, with the number of spanning cells (attribute), and \u2018 > \u2019. The hidden state attached to \u2018 < \u2019 is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.86197, "t": 668.2607, "r": 545.11511, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"id": 152, "text": "CNN Backbone Network.", "bbox": {"l": 320.81696, "t": 668.2607, "r": 431.90985, "b": 677.21707, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "A ResNet-18 CNN is the", "bbox": {"l": 439.49896, "t": 668.3802499999999, "r": 545.11255, "b": 677.2868100000001, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "backbone that receives the table image and encodes it as a", "bbox": {"l": 308.86197, "t": 680.33525, "r": 545.11499, "b": 689.24181, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "vector of predefined length. The network has been modified", "bbox": {"l": 308.86197, "t": 692.290253, "r": 545.11511, "b": 701.196815, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "by removing the linear and pooling layer, as we are not per-", "bbox": {"l": 308.86197, "t": 704.245255, "r": 545.11505, "b": 713.1518169999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}, "confidence": 0.8565718531608582, "cells": [{"id": 157, "text": "4", "bbox": {"l": 295.12097, "t": 734.133251, "r": 300.10226, "b": 743.039814, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, 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"coord_origin": "TOPLEFT"}}, {"id": 30, "text": "provide full description of", "bbox": {"l": 431.1738, "t": 157.80517999999995, "r": 498.87753000000004, "b": 163.54578000000004, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the table structure", "bbox": {"l": 440.5289, "t": 163.92516999999998, "r": 489.51827999999995, "b": 169.66576999999995, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Structure Tags", "bbox": {"l": 328.37479, "t": 178.25385000000006, "r": 367.72333, "b": 183.99445000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "BBoxes in sync", "bbox": {"l": 331.84451, "t": 123.90886999999998, "r": 373.67963, "b": 129.64948000000015, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "with tag sequence", "bbox": {"l": 331.84451, "t": 129.00885000000017, "r": 381.17786, "b": 134.74945000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Encoder", "bbox": {"l": 196.62633, "t": 88.11621000000002, "r": 219.42332, "b": 93.85681, "coord_origin": "TOPLEFT"}}, {"id": 36, 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The bounding boxes grabs the content from the PDF and inserts it in the structure.", "bbox": {"l": 50.111992, "t": 216.06035999999995, "r": 436.0134, "b": 224.96691999999996, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Input Image", "bbox": {"l": 74.253464, "t": 258.21472000000006, "r": 101.75846, "b": 264.17474000000004, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tokenised Tags", "bbox": {"l": 122.29972, "t": 258.34520999999995, "r": 157.83972, "b": 264.30524, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Multi-Head Attention", "bbox": {"l": 78.549347, "t": 371.38579999999996, "r": 125.68359000000001, "b": 377.04782, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Add", "bbox": {"l": 78.513298, "t": 391.31857, "r": 84.644547, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "& Normalisation", "bbox": {"l": 116.52705, "t": 391.31857, "r": 125.11079999999998, "b": 396.98059, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Feed Forward Network", "bbox": 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represent the input image.", "bbox": {"l": 50.112015, "t": 539.85738, "r": 286.36508, "b": 548.76393, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": 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Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "picture", "bbox": {"l": 74.30525970458984, "t": 77.91120147705078, "r": 519.9801025390625, "b": 183.70150756835938, "coord_origin": "TOPLEFT"}, "confidence": 0.9296807646751404, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Amount", "bbox": {"l": 102.50498, "t": 115.25214000000005, "r": 115.3461, "b": 118.44135000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Names", "bbox": {"l": 82.140205, "t": 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869195818901062, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}, {"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719564080238342, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}], "body": [{"label": "picture", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 74.30525970458984, "t": 77.91120147705078, "r": 519.9801025390625, "b": 183.70150756835938, "coord_origin": "TOPLEFT"}, "confidence": 0.9296807646751404, "cells": [{"id": 0, "text": "1.", "bbox": {"l": 81.688072, "t": 122.43970000000002, "r": 84.927567, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Item", "bbox": {"l": 86.54731, "t": 122.43970000000002, "r": 93.026291, "b": 125.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": 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"text": "The features are then passed to both the", "bbox": {"l": 50.112015, "t": 551.81337, "r": 205.84735, "b": 560.71992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Structure Decoder", "bbox": {"l": 208.01802, "t": 551.69382, "r": 286.36392, "b": 560.6501900000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "and", "bbox": {"l": 50.112015, "t": 563.76837, "r": 64.498009, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox Decoder", "bbox": {"l": 68.165016, "t": 563.64882, "r": 151.31288, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": ".", "bbox": {"l": 151.31302, "t": 563.76837, "r": 153.80367, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "During training, the", "bbox": {"l": 160.41884, "t": 563.76837, "r": 241.93283000000002, "b": 572.67493, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "Structure", "bbox": {"l": 245.59502, "t": 563.64882, "r": 286.362, "b": 572.60519, "coord_origin": "TOPLEFT"}}, {"id": 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Afterwards, a transformer en-", "bbox": {"l": 50.112015, "t": 587.6783800000001, "r": 286.36511, "b": 596.58493, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "coder and decoder architecture is employed to produce fea-", "bbox": {"l": 50.112015, "t": 599.63338, "r": 286.36508, "b": 608.53993, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "tures that are received by a linear layer, and the", "bbox": {"l": 50.112015, "t": 611.58838, "r": 240.43756000000002, "b": 620.4949300000001, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "Cell BBox", "bbox": {"l": 243.19801, "t": 611.46883, "r": 286.36597, "b": 620.4252, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Decoder. The linear layer is applied to the features to", "bbox": {"l": 50.112015, "t": 623.42482, "r": 286.36511, "b": 632.3812, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "predict the tags. Simultaneously, the Cell BBox Decoder", "bbox": {"l": 50.112015, "t": 635.37982, "r": 286.36508, "b": 644.3362, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "selects features referring to the data cells (\u2018", "bbox": {"l": 50.112015, "t": 647.45438, "r": 220.58205, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "<", "bbox": {"l": 220.57802000000004, "t": 647.29497, "r": 228.32693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "td", "bbox": {"l": 228.32700999999997, "t": 647.45438, "r": 236.07791000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": ">", "bbox": {"l": 236.07802000000004, "t": 647.29497, "r": 243.82693, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "\u2019, \u2018", "bbox": {"l": 243.82602, "t": 647.45438, "r": 255.29298000000003, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "<", "bbox": {"l": 255.29102000000003, "t": 647.29497, "r": 263.03992, "b": 656.14175, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "\u2019) and", "bbox": {"l": 263.04001, "t": 647.45438, "r": 286.36246, "b": 656.36093, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "passes them through an attention network, an MLP, and a", "bbox": {"l": 50.112015, "t": 659.40938, "r": 286.36511, "b": 668.31594, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "linear layer to predict the bounding boxes.", "bbox": {"l": 50.112015, "t": 671.36438, "r": 218.46996, "b": 680.27094, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives \u2018tokenized tags\u2019 of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (\u2018 < td > \u2019, \u2018 < \u2019) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 545.11505, "b": 294.3069499999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"id": 135, "text": "forming classification, and adding an adaptive pooling", "bbox": {"l": 308.862, "t": 249.53441999999995, "r": 523.05786, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "layer", "bbox": {"l": 525.19983, "t": 249.53441999999995, "r": 545.11505, "b": 258.44097999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "of size 28*28. 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ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86194, "t": 297.33981, "r": 545.11511, "b": 413.96182, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"id": 146, "text": "Structure Decoder.", "bbox": {"l": 320.81696, "t": 297.33981, "r": 400.86649, "b": 306.2962, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "The transformer architecture of", "bbox": {"l": 403.91394, "t": 297.45938, "r": 528.33685, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "this", "bbox": {"l": 530.7179, "t": 297.45938, "r": 545.11383, "b": 306.36594, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "component is based on the work proposed in [31].", "bbox": {"l": 308.86194, "t": 309.41437, "r": 517.5285, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "After", "bbox": {"l": 524.09387, "t": 309.41437, "r": 545.11493, "b": 318.32092, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "extensive experimentation, the", "bbox": {"l": 308.86194, "t": 321.36934999999994, "r": 432.35833999999994, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Structure Decoder", "bbox": {"l": 435.81995000000006, "t": 321.45901, "r": 510.29041, "b": 330.04678, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "is", "bbox": {"l": 513.97797, "t": 321.36934999999994, "r": 520.62305, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "mod-", "bbox": {"l": 524.08008, "t": 321.36934999999994, "r": 545.11115, "b": 330.27591, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "eled as a transformer encoder with two encoder layers", "bbox": {"l": 308.86197, "t": 333.32434, "r": 527.76013, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "and", "bbox": {"l": 530.729, "t": 333.32434, "r": 545.11499, "b": 342.2309, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "a transformer decoder made from a stack of 4 decoder", "bbox": {"l": 308.86197, "t": 345.27933, "r": 526.85352, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "lay-", "bbox": {"l": 529.62311, "t": 345.27933, "r": 545.11493, "b": 354.18588, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "ers that comprise mainly of multi-head attention and", "bbox": {"l": 308.86197, "t": 357.23532, "r": 524.51245, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "feed", "bbox": {"l": 527.96948, "t": 357.23532, "r": 545.11511, "b": 366.14188, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "forward layers.", "bbox": {"l": 308.86197, "t": 369.19031000000007, "r": 370.39096, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "This configuration uses fewer layers", "bbox": {"l": 377.44449, "t": 369.19031000000007, "r": 526.91339, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "and", "bbox": {"l": 530.72906, "t": 369.19031000000007, "r": 545.11505, "b": 378.09685999999994, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "heads in comparison to networks applied to other", "bbox": {"l": 308.86197, "t": 381.14529000000005, "r": 505.46395999999993, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "problems", "bbox": {"l": 508.03430000000003, "t": 381.14529000000005, "r": 545.11511, "b": 390.05185, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "(e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d),", "bbox": {"l": 308.86197, "t": 393.10028, "r": 517.68799, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "some-", "bbox": {"l": 520.76642, "t": 393.10028, "r": 545.11499, "b": 402.00684, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "thing which we relate to the simplicity of table images.", "bbox": {"l": 308.86197, "t": 405.05526999999995, "r": 528.01935, "b": 413.96182, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. \u201cScene Understanding\u201d, \u201cImage Captioning\u201d), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86197, "t": 417.11426, "r": 545.11511, "b": 545.57271, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"id": 169, "text": "The transformer encoder receives an encoded", "bbox": {"l": 320.81696, "t": 417.11426, "r": 515.49609, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "image", "bbox": {"l": 520.7663, "t": 417.11426, "r": 545.11487, "b": 426.02081, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "from the", "bbox": {"l": 308.86197, "t": 429.0692399999999, "r": 343.72107, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "CNN Backbone Network", "bbox": {"l": 347.03796, "t": 429.15891, "r": 446.45471000000003, "b": 437.74667, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "and refines it", "bbox": {"l": 449.93996999999996, "t": 429.0692399999999, "r": 503.06055000000003, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "through", "bbox": {"l": 506.37808, "t": 429.0692399999999, "r": 537.3717, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a", "bbox": {"l": 540.68927, "t": 429.0692399999999, "r": 545.11267, "b": 437.9758, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "multi-head dot-product attention layer, followed by a", "bbox": {"l": 308.86197, "t": 441.02423, "r": 522.78894, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "Feed", "bbox": {"l": 525.7478, "t": 441.02423, "r": 545.11511, "b": 449.93079, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "Forward Network.", "bbox": {"l": 308.86197, "t": 452.97922, "r": 384.14929, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "During training, the transformer", "bbox": {"l": 393.37466, "t": 452.97922, "r": 527.84985, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "de-", "bbox": {"l": 532.39282, "t": 452.97922, "r": 545.11505, "b": 461.88577, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "coder receives as input the output feature produced by", "bbox": {"l": 308.86197, "t": 464.93521, "r": 529.7627, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "the", "bbox": {"l": 532.94073, "t": 464.93521, "r": 545.11505, "b": 473.84177, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "transformer encoder, and the tokenized input of the", "bbox": {"l": 308.86197, "t": 476.8902, "r": 514.17126, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "HTML", "bbox": {"l": 516.89105, "t": 476.8902, "r": 545.11511, "b": 485.79675, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "ground-truth tags. Using a stack of multi-head attention", "bbox": {"l": 308.86197, "t": 488.84518, "r": 527.63068, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "lay-", "bbox": {"l": 529.62317, "t": 488.84518, "r": 545.11499, "b": 497.75174, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "ers, different aspects of the tag sequence could be", "bbox": {"l": 308.86197, "t": 500.80017, "r": 508.3630999999999, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "inferred.", "bbox": {"l": 511.09286000000003, "t": 500.80017, "r": 545.11511, "b": 509.70673, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "This is achieved by each attention head on a layer operating", "bbox": {"l": 308.86197, "t": 512.7551599999999, "r": 545.11499, "b": 521.6617100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "in a different subspace, and then combining altogether their", "bbox": {"l": 308.86197, "t": 524.71115, "r": 545.11511, "b": 533.61771, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "attention score.", "bbox": {"l": 308.86197, "t": 536.66615, "r": 369.73349, "b": 545.57271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86194, "t": 548.6046, "r": 545.11511, "b": 653.27271, "coord_origin": "TOPLEFT"}, "confidence": 0.9869195818901062, "cells": [{"id": 192, "text": "Cell BBox Decoder.", "bbox": {"l": 320.81696, "t": 548.6046, "r": 404.76184, "b": 557.56097, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "Our architecture allows to simul-", "bbox": {"l": 410.34094, "t": 548.72415, "r": 545.11505, "b": 557.63071, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "taneously predict HTML tags and bounding boxes for each", "bbox": {"l": 308.86194, "t": 560.68015, "r": 545.11493, "b": 569.5867000000001, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "table cell without the need of a separate object detector end", "bbox": {"l": 308.86194, "t": 572.6351500000001, "r": 545.11511, "b": 581.5417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "to end. This approach is inspired by DETR [1] which em-", "bbox": {"l": 308.86194, "t": 584.59015, "r": 545.11493, "b": 593.4967, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "ploys a Transformer Encoder, and Decoder that looks for", "bbox": {"l": 308.86194, "t": 596.54515, "r": 545.11499, "b": 605.45171, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "a specific number of object queries (potential object detec-", "bbox": {"l": 308.86194, "t": 608.50015, "r": 545.11505, "b": 617.40671, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions). As our model utilizes a transformer architecture, the", "bbox": {"l": 308.86194, "t": 620.45515, "r": 545.11505, "b": 629.36171, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "hidden state of the", "bbox": {"l": 308.86194, "t": 632.41115, "r": 381.67859, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "<", "bbox": {"l": 383.99695, "t": 632.25174, "r": 391.74585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "td", "bbox": {"l": 391.74594, "t": 632.41115, "r": 399.49686, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ">", "bbox": {"l": 399.49695, "t": 632.25174, "r": 407.24585, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "\u2019 and \u2018", "bbox": {"l": 407.24594, "t": 632.41115, "r": 432.90958, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "<", "bbox": {"l": 432.90792999999996, "t": 632.25174, "r": 440.65683000000007, "b": 641.09853, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "\u2019 HTML structure tags be-", "bbox": {"l": 440.65691999999996, "t": 632.41115, "r": 545.11475, "b": 641.3177000000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "come the object query.", "bbox": {"l": 308.86194, "t": 644.3661500000001, "r": 398.96371, "b": 653.27271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > \u2019 and \u2018 < \u2019 HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86194, "t": 656.42516, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"id": 208, "text": "The encoding generated by the", "bbox": {"l": 320.81693, "t": 656.42516, "r": 444.34316999999993, "b": 665.33172, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "CNN Backbone Network", "bbox": {"l": 447.00591999999995, "t": 656.51482, "r": 545.1076, "b": 665.10258, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "along with the features acquired for every data cell from the", "bbox": {"l": 308.86194, "t": 668.38016, "r": 545.11505, "b": 677.2867200000001, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Transformer Decoder are then passed to the attention net-", "bbox": {"l": 308.86194, "t": 680.33516, "r": 545.11505, "b": 689.24172, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "work. The attention network takes both inputs and learns to", "bbox": {"l": 308.86194, "t": 692.290161, "r": 545.11505, "b": 701.196724, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "provide an attention weighted encoding. This weighted at-", "bbox": {"l": 308.86194, "t": 704.245163, "r": 545.11505, "b": 713.151726, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}, "confidence": 0.8719564080238342, "cells": [{"id": 214, "text": "5", "bbox": {"l": 295.12094, "t": 734.13316, "r": 300.10223, "b": 743.039722, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538503289222717, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.844183623790741, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538503289222717, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.844183623790741, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"id": 0, "text": "tention encoding is then multiplied to the encoded image to", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 286.36514, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "produce a feature for each table cell. Notice that this is dif-", "bbox": {"l": 50.112, "t": 87.16339000000005, "r": 286.36508, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ferent than the typical object detection problem where im-", "bbox": {"l": 50.112, "t": 99.11841000000004, "r": 286.36508, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "balances between the number of detections and the amount", "bbox": {"l": 50.112, "t": 111.07343000000003, "r": 286.36508, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "of objects may exist. In our case, we know up front that", "bbox": {"l": 50.112, "t": 123.02844000000005, "r": 286.36508, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "the produced detections always match with the table cells", "bbox": {"l": 50.112, "t": 134.98443999999995, "r": 286.36514, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "in number and correspondence.", "bbox": {"l": 50.112, "t": 146.93944999999997, "r": 175.16254, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.62445000000002, "r": 286.36511, "b": 240.26306, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"id": 7, "text": "The output features for each table cell are then fed", "bbox": {"l": 62.067001, "t": 159.62445000000002, "r": 286.36496, "b": 168.53101000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "into the feed-forward network (FFN). The FFN consists", "bbox": {"l": 50.112, "t": 171.58043999999995, "r": 286.36511, "b": 180.48699999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "bbox": {"l": 50.112, "t": 183.53545999999994, "r": 286.36511, "b": 192.44201999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "tion function) that predicts the normalized coordinates for", "bbox": {"l": 50.112, "t": 195.49048000000005, "r": 286.36511, "b": 204.39702999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "the bounding box of each table cell. Finally, the predicted", "bbox": {"l": 50.112, "t": 207.44550000000004, "r": 286.36511, "b": 216.35204999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "bounding boxes are classified based on whether they are", "bbox": {"l": 50.112, "t": 219.40051000000005, "r": 286.36511, "b": 228.30706999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "empty or not using a linear layer.", "bbox": {"l": 50.112, "t": 231.35650999999996, "r": 181.54855, "b": 240.26306, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92193999999995, "r": 286.36572, "b": 444.2309, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"id": 14, "text": "Loss Functions.", "bbox": {"l": 62.067001, "t": 243.92193999999995, "r": 129.21492, "b": 252.87829999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "We formulate a multi-task loss Eq. 2", "bbox": {"l": 134.451, "t": 244.04150000000004, "r": 286.36078, "b": 252.94806000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to train our network. The Cross-Entropy loss (denoted as", "bbox": {"l": 50.112007, "t": 255.99652000000003, "r": 286.36511, "b": 264.90308000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "l$_{s}$", "bbox": {"l": 50.112007, "t": 267.79309, "r": 56.84528, "b": 276.63989000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": ") is used to train the", "bbox": {"l": 57.343006, "t": 267.95250999999996, "r": 135.39996, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Structure Decoder", "bbox": {"l": 137.735, "t": 268.04218000000003, "r": 211.07965, "b": 276.62994000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "which predicts the", "bbox": {"l": 213.63699, "t": 267.95250999999996, "r": 286.36395, "b": 276.85907, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "structure tokens. As for the", "bbox": {"l": 50.112, "t": 279.90747, "r": 158.82388, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Cell BBox Decoder", "bbox": {"l": 161.31799, "t": 279.99712999999997, "r": 238.79712, "b": 288.58493, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "it is trained", "bbox": {"l": 241.521, "t": 279.90747, "r": 286.36264, "b": 288.81406, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "with a combination of losses denoted as", "bbox": {"l": 50.112, "t": 291.86249, "r": 211.3766, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "l$_{box}$", "bbox": {"l": 214.271, "t": 291.70309, "r": 229.19780000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": ".", "bbox": {"l": 229.696, "t": 291.86249, "r": 232.18665000000001, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "l$_{box}$", "bbox": {"l": 236.49001, "t": 291.70309, "r": 251.41681000000003, "b": 300.54987, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "consists", "bbox": {"l": 254.81099999999998, "t": 291.86249, "r": 286.36255, "b": 300.76904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of the generally used", "bbox": {"l": 50.112, "t": 303.81747, "r": 137.45412, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "l$_{1}$", "bbox": {"l": 141.298, "t": 303.65808, "r": 148.24258, "b": 312.50485, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "loss for object detection and the", "bbox": {"l": 152.58601, "t": 303.81747, "r": 286.36377, "b": 312.72403, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "IoU loss (", "bbox": {"l": 50.112015, "t": 315.77245999999997, "r": 89.683464, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "l$_{iou}$", "bbox": {"l": 89.68602, "t": 315.61307, "r": 104.12046, "b": 324.45984, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ") to be scale invariant as explained in [25]. In", "bbox": {"l": 104.61802, "t": 315.77245999999997, "r": 286.36572, "b": 324.67902, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "comparison to DETR, we do not use the Hungarian algo-", "bbox": {"l": 50.112019, "t": 327.72845, "r": 286.36511, "b": 336.6350100000001, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "rithm [15] to match the predicted bounding boxes with the", "bbox": {"l": 50.112019, "t": 339.68344, "r": 286.36508, "b": 348.59, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "ground-truth boxes, as we have already achieved a one-to-", "bbox": {"l": 50.112019, "t": 351.63843, "r": 286.36511, "b": 360.54498, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "one match through two steps: 1) Our token input sequence", "bbox": {"l": 50.112019, "t": 363.59341, "r": 286.36508, "b": 372.49996999999996, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is naturally ordered, therefore the hidden states of the table", "bbox": {"l": 50.112019, "t": 375.5484, "r": 286.36511, "b": 384.45496, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "data cells are also in order when they are provided as in-", "bbox": {"l": 50.112019, "t": 387.50339, "r": 286.36514, "b": 396.40994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "put to the", "bbox": {"l": 50.112019, "t": 399.45938, "r": 88.68721, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Cell BBox Decoder", "bbox": {"l": 91.646019, "t": 399.54904, "r": 170.0517, "b": 408.13681, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", and 2) Our bounding boxes", "bbox": {"l": 170.05103, "t": 399.45938, "r": 286.36438, "b": 408.36594, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "generation mechanism (see Sec.", "bbox": {"l": 50.112022, "t": 411.41437, "r": 181.96703, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "3)", "bbox": {"l": 189.09029, "t": 411.41437, "r": 197.74918, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ensures a one-to-one", "bbox": {"l": 200.34789, "t": 411.41437, "r": 286.36511, "b": 420.32092, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "mapping between the cell content and its bounding box for", "bbox": {"l": 50.112022, "t": 423.36934999999994, "r": 286.36511, "b": 432.27591, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "all post-processed datasets.", "bbox": {"l": 50.112022, "t": 435.32434, "r": 158.2959, "b": 444.2309, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112022, "t": 448.01035, "r": 286.36499, "b": 468.87189, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"id": 49, "text": "The loss used to train the TableFormer can be defined as", "bbox": {"l": 62.067024, "t": 448.01035, "r": 286.36499, "b": 456.9169, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "following:", "bbox": {"l": 50.112022, "t": 459.96533, "r": 91.377113, "b": 468.87189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33002, "t": 493.28094, "r": 286.36243, "b": 517.07172, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"id": 51, "text": "l$_{box}$", "bbox": {"l": 125.71502, "t": 493.28094, "r": 140.64182, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "=", "bbox": {"l": 143.90701, "t": 493.28094, "r": 151.65593, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "\u03bb$_{iou}$l$_{iou}$", "bbox": {"l": 154.42302, "t": 493.28094, "r": 186.62846, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "+", "bbox": {"l": 189.34003, "t": 493.28094, "r": 197.08894, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\u03bb$_{l}$$_{1}$", "bbox": {"l": 199.30302, "t": 493.28094, "r": 211.64659, "b": 502.12772, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "l", "bbox": {"l": 124.33002, "t": 508.22495, "r": 127.30286, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "=", "bbox": {"l": 130.26602, "t": 508.22495, "r": 138.01494, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "\u03bbl$_{s}$", "bbox": {"l": 140.78203, "t": 508.22495, "r": 153.32629, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "+ (1", "bbox": {"l": 156.03903, "t": 508.22495, "r": 174.85541, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "\u2212", "bbox": {"l": 177.07103, "t": 507.66702, "r": 184.81995, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u03bb", "bbox": {"l": 187.03304, "t": 508.22495, "r": 192.84422, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": ")", "bbox": {"l": 192.84503, "t": 508.22495, "r": 196.71948, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "l$_{box}$", "bbox": {"l": 196.71902, "t": 508.22495, "r": 211.64583, "b": 517.07172, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(1)", "bbox": {"l": 274.74603, "t": 501.01132, "r": 286.36243, "b": 509.91788, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "l$_{box}$ = \u03bb$_{iou}$l$_{iou}$ + \u03bb$_{l}$$_{1}$ l = \u03bbl$_{s}$ + (1 \u2212 \u03bb ) l$_{box}$ (1)"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.11203, "t": 530.5920100000001, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"id": 65, "text": "where", "bbox": {"l": 50.11203, "t": 531.30933, "r": 74.450661, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\u03bb", "bbox": {"l": 76.941032, "t": 531.14993, "r": 82.75222, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "\u2208", "bbox": {"l": 85.520035, "t": 530.5920100000001, "r": 92.162102, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "[0, 1], and", "bbox": {"l": 94.653038, "t": 531.30933, "r": 135.59932, "b": 540.21588, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "\u03bb$_{iou}$, \u03bb$_{l}$$_{1}$", "bbox": {"l": 138.09004, "t": 531.14993, "r": 172.63162, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "\u2208$_{R}$", "bbox": {"l": 175.89705, "t": 530.5920100000001, "r": 192.50104, "b": 539.9967, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "are hyper-parameters.", "bbox": {"l": 194.99205, "t": 531.30933, "r": 281.59692, "b": 540.21588, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where \u03bb \u2208 [0, 1], and \u03bb$_{iou}$, \u03bb$_{l}$$_{1}$ \u2208$_{R}$ are hyper-parameters."}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112045, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"id": 72, "text": "5.", "bbox": {"l": 50.112045, "t": 555.91689, "r": 57.92831799999999, "b": 566.66461, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Experimental Results", "bbox": {"l": 68.350014, "t": 555.91689, "r": 171.98335, "b": 566.66461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112045, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9538503289222717, "cells": [{"id": 74, "text": "5.1.", "bbox": {"l": 50.112045, "t": 576.26433, "r": 64.693237, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Implementation Details", "bbox": {"l": 74.414032, "t": 576.26433, "r": 179.17502, "b": 586.1163799999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112045, "t": 595.73433, "r": 286.36517, "b": 640.50688, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"id": 76, "text": "TableFormer uses ResNet-18 as the", "bbox": {"l": 62.067047, "t": 595.73433, "r": 202.97806, "b": 604.64088, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "CNN Backbone Net-", "bbox": {"l": 205.38405, "t": 595.82399, "r": 286.36008, "b": 604.41174, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "work", "bbox": {"l": 50.112045, "t": 607.77899, "r": 70.037247, "b": 616.3667399999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": ". The input images are resized to 448*448 pixels and", "bbox": {"l": 70.037048, "t": 607.68933, "r": 286.36496, "b": 616.59589, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the feature map has a dimension of 28*28. Additionally, we", "bbox": {"l": 50.112049, "t": 619.64433, "r": 286.36517, "b": 628.55089, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "enforce the following input constraints:", "bbox": {"l": 50.112049, "t": 631.60033, "r": 207.03294, "b": 640.50688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661049, "t": 653.828, "r": 286.36246, "b": 678.39588, "coord_origin": "TOPLEFT"}, "confidence": 0.844183623790741, "cells": [{"id": 82, "text": "Image width and height", "bbox": {"l": 91.661049, "t": 654.54532, "r": 186.01683, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "\u2264", "bbox": {"l": 188.50705, "t": 653.828, "r": 196.25597, "b": 663.2327, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1024 pixels", "bbox": {"l": 198.74605, "t": 654.54532, "r": 244.81310999999997, "b": 663.45187, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Structural tags length", "bbox": {"l": 101.01604, "t": 669.48932, "r": 186.24606, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "\u2264", "bbox": {"l": 188.73605, "t": 668.77201, "r": 196.48497, "b": 678.1767, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "512 tokens.", "bbox": {"l": 198.97505, "t": 669.48932, "r": 244.81296999999998, "b": 678.39588, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "(2)", "bbox": {"l": 274.74606, "t": 662.11731, "r": 286.36246, "b": 671.02388, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"id": 89, "text": "Although input constraints are used also by other methods,", "bbox": {"l": 50.112061, "t": 692.290314, "r": 286.36514, "b": 701.196877, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "such as EDD, ours are less restrictive due to the improved", "bbox": {"l": 50.112061, "t": 704.245316, "r": 286.36514, "b": 713.151878, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 108.0249, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"id": 91, "text": "runtime performance and lower memory footprint of Table-", "bbox": {"l": 308.86206, "t": 75.20830999999998, "r": 545.11523, "b": 84.11487, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Former.", "bbox": {"l": 308.86206, "t": 87.16332999999997, "r": 339.98523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "This allows to utilize input samples with longer", "bbox": {"l": 346.88931, "t": 87.16332999999997, "r": 545.11523, "b": 96.06988999999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "sequences and images with larger dimensions.", "bbox": {"l": 308.86206, "t": 99.11835000000008, "r": 492.96097, "b": 108.0249, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86206, "t": 116.22937000000002, "r": 545.1153, "b": 328.37405, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"id": 95, "text": "The Transformer Encoder consists of two \u201cTransformer", "bbox": {"l": 320.81705, "t": 116.22937000000002, "r": 545.11499, "b": 125.13593000000003, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Encoder Layers\u201d, with an input feature size of 512, feed", "bbox": {"l": 308.86206, "t": 128.18439, "r": 545.11517, "b": 137.09094000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "forward network of 1024, and 4 attention heads. As for the", "bbox": {"l": 308.86206, "t": 140.13940000000002, "r": 545.11505, "b": 149.04596000000004, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Transformer Decoder it is composed of four \u201cTransformer", "bbox": {"l": 308.86206, "t": 152.09442, "r": 545.11511, "b": 161.00098000000003, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Decoder Layers\u201d with similar input and output dimensions", "bbox": {"l": 308.86206, "t": 164.04944, "r": 545.11517, "b": 172.95599000000004, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "as the \u201cTransformer Encoder Layers\u201d.", "bbox": {"l": 308.86206, "t": 176.00543000000005, "r": 467.21756000000005, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Even though our", "bbox": {"l": 475.43671, "t": 176.00543000000005, "r": 545.11511, "b": 184.91198999999995, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "model uses fewer layers and heads than the default imple-", "bbox": {"l": 308.86206, "t": 187.96045000000004, "r": 545.11511, "b": 196.86699999999996, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "mentation parameters, our extensive experimentation has", "bbox": {"l": 308.86206, "t": 199.91547000000003, "r": 545.11511, "b": 208.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "proved this setup to be more suitable for table images. We", "bbox": {"l": 308.86206, "t": 211.87048000000004, "r": 545.11517, "b": 220.77704000000006, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "attribute this finding to the inherent design of table im-", "bbox": {"l": 308.86206, "t": 223.82550000000003, "r": 545.11511, "b": 232.73206000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "ages, which contain mostly lines and text, unlike the more", "bbox": {"l": 308.86206, "t": 235.78052000000002, "r": 545.11511, "b": 244.68706999999995, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "elaborate content present in other scopes (e.g. the COCO", "bbox": {"l": 308.86206, "t": 247.73650999999995, "r": 545.11523, "b": 256.64306999999997, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "dataset).", "bbox": {"l": 308.86206, "t": 259.69152999999994, "r": 342.3364, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Moreover, we have added ResNet blocks to the", "bbox": {"l": 348.95157, "t": 259.69152999999994, "r": 545.11517, "b": 268.59808, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "bbox": {"l": 308.86206, "t": 271.64655000000005, "r": 545.11517, "b": 280.55310000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "This prevents a decoder having a stronger influence over the", "bbox": {"l": 308.86206, "t": 283.6015300000001, "r": 545.1153, "b": 292.50809, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "learned weights which would damage the other prediction", "bbox": {"l": 308.86206, "t": 295.55652, "r": 545.11511, "b": 304.46307, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "task (structure vs bounding boxes), but learn task specific", "bbox": {"l": 308.86206, "t": 307.51151, "r": 545.11511, "b": 316.41806, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "bbox": {"l": 308.86206, "t": 319.4674999999999, "r": 532.48267, "b": 328.37405, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Transformer Encoder consists of two \u201cTransformer Encoder Layers\u201d, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four \u201cTransformer Decoder Layers\u201d with similar input and output dimensions as the \u201cTransformer Encoder Layers\u201d. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.86203, "t": 336.57751, "r": 545.11517, "b": 429.16998, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"id": 115, "text": "For training, TableFormer is trained with 3 Adam opti-", "bbox": {"l": 320.81705, "t": 336.57751, "r": 545.11499, "b": 345.48407000000003, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "mizers, each one for the", "bbox": {"l": 308.86206, "t": 348.5325000000001, "r": 403.7359, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "CNN Backbone Network", "bbox": {"l": 406.07605, "t": 348.62216, "r": 503.54016, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": ",", "bbox": {"l": 503.53906, "t": 348.5325000000001, "r": 506.02972, "b": 357.43906, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Structure", "bbox": {"l": 508.40004999999996, "t": 348.62216, "r": 545.11224, "b": 357.20993, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Decoder", "bbox": {"l": 308.86206, "t": 360.57715, "r": 343.1633, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": ", and", "bbox": {"l": 343.16306, "t": 360.48749, "r": 362.2016, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 364.28604, "t": 360.57715, "r": 440.93829, "b": 369.16492000000005, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". Taking the PubTabNet as", "bbox": {"l": 440.93903, "t": 360.48749, "r": 545.10797, "b": 369.39404, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "an example for our parameter set up, the initializing learn-", "bbox": {"l": 308.86203, "t": 372.44247, "r": 545.11511, "b": 381.34903, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "bbox": {"l": 308.86203, "t": 384.3984699999999, "r": 545.11517, "b": 393.30502, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "\u03bb", "bbox": {"l": 308.86203, "t": 396.19406000000004, "r": 314.67322, "b": 405.04083, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "set to 0.5.", "bbox": {"l": 318.65802, "t": 396.35345, "r": 360.39139, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Afterwards, we reduce the learning rate to", "bbox": {"l": 367.96295, "t": 396.35345, "r": 545.10803, "b": 405.2600100000001, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "bbox": {"l": 308.86203, "t": 408.30844, "r": 545.11511, "b": 417.215, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "convergence.", "bbox": {"l": 308.86203, "t": 420.26343, "r": 360.9664, "b": 429.16998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.86203, "t": 437.37441999999993, "r": 545.11523, "b": 553.87689, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"id": 131, "text": "TableFormer is implemented with PyTorch and Torchvi-", "bbox": {"l": 320.81702, "t": 437.37441999999993, "r": 545.11499, "b": 446.28098, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "sion libraries [22].", "bbox": {"l": 308.86203, "t": 449.32941, "r": 384.62759, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "To speed up the inference, the image", "bbox": {"l": 391.37228, "t": 449.32941, "r": 545.11511, "b": 458.23596, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "undergoes a single forward pass through the", "bbox": {"l": 308.86203, "t": 461.28439, "r": 494.00693000000007, "b": 470.19095, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "CNN Back-", "bbox": {"l": 498.07803, "t": 461.37405, "r": 545.11145, "b": 469.96182, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "bone Network", "bbox": {"l": 308.86203, "t": 473.32904, "r": 364.44336, "b": 481.91681, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "and transformer encoder. This eliminates the", "bbox": {"l": 367.06104, "t": 473.23938, "r": 545.11267, "b": 482.14594, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "overhead of generating the same features for each decoding", "bbox": {"l": 308.86203, "t": 485.19437, "r": 545.11511, "b": 494.10092, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "step. Similarly, we employ a \u2019caching\u2019 technique to preform", "bbox": {"l": 308.86203, "t": 497.14935, "r": 545.11523, "b": 506.05591, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "faster autoregressive decoding. This is achieved by storing", "bbox": {"l": 308.86203, "t": 509.10535, "r": 545.11511, "b": 518.0119, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "the features of decoded tokens so we can reuse them for", "bbox": {"l": 308.86203, "t": 521.06033, "r": 545.11517, "b": 529.9668899999999, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "each time step. Therefore, we only compute the attention", "bbox": {"l": 308.86203, "t": 533.01532, "r": 545.11517, "b": 541.9218900000001, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "for each new tag.", "bbox": {"l": 308.86203, "t": 544.97034, "r": 377.21548, "b": 553.87689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a \u2019caching\u2019 technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.86203, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"id": 144, "text": "5.2.", "bbox": {"l": 308.86203, "t": 579.55432, "r": 323.9046, "b": 589.40637, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Generalization", "bbox": {"l": 333.93301, "t": 579.55432, "r": 397.44281, "b": 589.40637, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.86203, "t": 603.44933, "r": 545.11517, "b": 672.13189, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"id": 146, "text": "TableFormer is evaluated on three major publicly avail-", "bbox": {"l": 320.81702, "t": 603.44933, "r": 545.11493, "b": 612.3558800000001, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "able datasets of different nature to prove the generalization", "bbox": {"l": 308.86203, "t": 615.40433, "r": 545.11511, "b": 624.31088, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "and effectiveness of our model. The datasets used for eval-", "bbox": {"l": 308.86203, "t": 627.35933, "r": 545.11517, "b": 636.26588, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "bbox": {"l": 308.86203, "t": 639.31433, "r": 545.11511, "b": 648.22089, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "stem from the scientific, financial and general domains re-", "bbox": {"l": 308.86203, "t": 651.27032, "r": 545.11517, "b": 660.17688, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "spectively.", "bbox": {"l": 308.86203, "t": 663.22533, "r": 350.70493, "b": 672.13189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.86203, "t": 680.33533, "r": 545.11523, "b": 713.152893, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"id": 152, "text": "We also share our baseline results on the challenging", "bbox": {"l": 320.81702, "t": 680.33533, "r": 545.11505, "b": 689.24189, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "SynthTabNet dataset.", "bbox": {"l": 308.86203, "t": 692.290329, "r": 396.21411, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "Throughout our experiments, the", "bbox": {"l": 406.40585, "t": 692.290329, "r": 545.11523, "b": 701.196892, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "same parameters stated in Sec. 5.1 are utilized.", "bbox": {"l": 308.86203, "t": 704.246323, "r": 495.93982, "b": 713.152893, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"id": 156, "text": "6", "bbox": {"l": 295.12103, "t": 734.133327, "r": 300.10233, "b": 743.03989, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 247.74349999999998, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "All", "bbox": {"l": 264.54044, "t": 426.66736, "r": 277.27264, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "EDD", "bbox": {"l": 81.612, "t": 443.62436, "r": 102.08514, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "PTN", "bbox": {"l": 134.87206, "t": 443.62436, "r": 153.69141, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "91.1", "bbox": {"l": 176.56554, "t": 443.62436, "r": 194.00009, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "88.7", "bbox": {"l": 220.82938000000001, "t": 443.62436, "r": 238.26393, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "89.9", "bbox": {"l": 262.18414, "t": 443.62436, "r": 279.61868, "b": 452.53091, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "GTE", "bbox": 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"TOPLEFT"}}, {"id": 72, "text": "95.0", "bbox": {"l": 220.83495, "t": 468.13336, "r": 238.26950000000002, "b": 477.03992, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "96.75", "bbox": {"l": 259.698, "t": 468.01379, "r": 282.11386, "b": 476.97018, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "EDD", "bbox": {"l": 81.612, "t": 483.32635, "r": 102.08514, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "FTN", "bbox": {"l": 134.87206, "t": 483.32635, "r": 153.69141, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "88.4", "bbox": {"l": 176.56554, "t": 483.32635, "r": 194.00009, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "92.08", "bbox": {"l": 218.33870999999996, "t": 483.32635, "r": 240.75455999999997, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "90.6", "bbox": {"l": 262.18411, "t": 483.32635, "r": 279.61865, "b": 492.23291, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "GTE", "bbox": {"l": 82.165001, "t": 495.28134, "r": 101.5323, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "FTN", "bbox": {"l": 134.86716, "t": 495.28134, "r": 153.68651, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "-", "bbox": {"l": 183.62411, "t": 495.28134, "r": 186.94167, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "-", "bbox": {"l": 227.88795000000002, "t": 495.28134, "r": 231.20551, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "87.14", "bbox": {"l": 259.69855, "t": 495.28134, "r": 282.11441, "b": 504.1879, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "GTE (FT)", "bbox": {"l": 71.789001, "t": 507.23633, "r": 111.90838999999998, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "FTN", "bbox": {"l": 134.86221, "t": 507.23633, "r": 153.68156, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "-", "bbox": {"l": 183.62914, "t": 507.23633, "r": 186.94669, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "-", "bbox": {"l": 227.89297, "t": 507.23633, "r": 231.21053000000003, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "91.02", "bbox": {"l": 259.6936, "t": 507.23633, "r": 282.10947, "b": 516.14288, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 519.1913099999999, "r": 117.38329000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "FTN", "bbox": {"l": 134.86766, "t": 519.1913099999999, "r": 153.68701, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "97.5", "bbox": {"l": 176.57111, "t": 519.1913099999999, "r": 194.00566, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "96.0", "bbox": {"l": 220.83495, "t": 519.1913099999999, "r": 238.26950000000002, "b": 528.0978700000001, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "96.8", "bbox": {"l": 262.189, "t": 519.0717500000001, "r": 279.62354, "b": 528.02814, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "EDD", "bbox": {"l": 81.612, "t": 536.49837, "r": 102.08514, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "TB", "bbox": {"l": 137.91064, "t": 536.49837, "r": 150.64285, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "86.0", "bbox": {"l": 176.56554, "t": 536.49837, "r": 194.00009, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "-", "bbox": {"l": 227.89285, "t": 536.49837, "r": 231.21040000000002, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "86.0", "bbox": {"l": 262.18411, "t": 536.49837, "r": 279.61865, "b": 545.40492, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 548.45436, "r": 117.38329000000002, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "TB", "bbox": {"l": 137.90625, "t": 548.45436, "r": 150.63846, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "89.6", "bbox": {"l": 176.57111, "t": 548.45436, "r": 194.00566, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "-", "bbox": {"l": 227.88845999999998, "t": 548.45436, "r": 231.20601, "b": 557.36092, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "89.6", "bbox": {"l": 262.189, "t": 548.3348100000001, "r": 279.62354, "b": 557.2911799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "TableFormer", "bbox": {"l": 66.315002, "t": 568.00237, "r": 117.38329000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "STN", "bbox": {"l": 134.86766, "t": 568.00237, "r": 153.68701, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "96.9", "bbox": {"l": 176.57111, "t": 568.00237, "r": 194.00566, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "95.7", "bbox": {"l": 220.83495, "t": 568.00237, "r": 238.26950000000002, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "96.7", "bbox": {"l": 262.1897, "t": 568.00237, "r": 279.62424, "b": 576.90892, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", "bbox": {"l": 498.1713, "t": 282.45676, "r": 515.60583, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 294.53131, "r": 377.86331, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "SynthTabNet", "bbox": {"l": 389.81842, "t": 294.53131, "r": 442.15194999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "87.7", "bbox": {"l": 455.63135, "t": 294.53131, "r": 473.06589, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "-", "bbox": {"l": 505.22515999999996, "t": 294.53131, "r": 508.54268999999994, "b": 303.43787, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "Table 3:", "bbox": {"l": 308.862, "t": 316.44931, "r": 341.49951, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Cell Bounding Box detection results on PubTab-", "bbox": {"l": 348.60284, "t": 316.44931, "r": 545.11517, "b": 325.35587, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "Net, and FinTabNet. PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, "t": 558.21037, "r": 512.117, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Tabula", "bbox": {"l": 357.68201, "t": 575.16736, "r": 384.3519, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "78.0", "bbox": {"l": 413.90097, "t": 575.16736, "r": 431.33550999999994, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "57.8", "bbox": {"l": 458.16479000000004, "t": 575.16736, "r": 475.59933000000007, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "67.9", "bbox": {"l": 497.0289, "t": 575.16736, "r": 514.46344, "b": 584.0739100000001, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "Traprange", "bbox": {"l": 350.72299, "t": 587.12236, "r": 391.31064, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "60.8", "bbox": {"l": 413.90582, "t": 587.12236, "r": 431.34036, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "49.9", "bbox": {"l": 458.16965, "t": 587.12236, "r": 475.60419, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "55.4", "bbox": {"l": 497.03374999999994, "t": 587.12236, "r": 514.46832, "b": 596.02892, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Camelot", "bbox": {"l": 354.13599, "t": 599.07835, "r": 387.89923, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "80.0", "bbox": {"l": 413.90161, "t": 599.07835, "r": 431.33615, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "66.0", "bbox": {"l": 458.16544, "t": 599.07835, "r": 475.59998, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "73.0", "bbox": {"l": 497.02954000000005, "t": 599.07835, "r": 514.46411, "b": 607.98491, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Acrobat Pro", "bbox": {"l": 346.55899, "t": 611.03336, "r": 395.47534, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "68.9", "bbox": {"l": 413.90616, "t": 611.03336, "r": 431.34069999999997, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "61.8", "bbox": {"l": 458.16998000000007, "t": 611.03336, "r": 475.60452, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "65.3", "bbox": {"l": 497.03409, "t": 611.03336, "r": 514.46863, "b": 619.93991, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "EDD", "bbox": {"l": 360.78101, "t": 622.9883600000001, "r": 381.25415, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "91.2", "bbox": {"l": 413.90158, "t": 622.9883600000001, "r": 431.33612, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "85.4", "bbox": {"l": 458.16541, "t": 622.9883600000001, "r": 475.59995000000004, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "88.3", "bbox": {"l": 497.0295100000001, "t": 622.9883600000001, "r": 514.46405, "b": 631.89491, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "TableFormer", "bbox": {"l": 345.483, "t": 634.94336, "r": 396.5513, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "95.4", "bbox": {"l": 413.90616, "t": 634.94336, "r": 431.34069999999997, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "90.1", "bbox": {"l": 458.16998000000007, "t": 634.94336, "r": 475.60452, "b": 643.84991, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "93.6", "bbox": {"l": 497.03400000000005, "t": 634.82381, "r": 514.46857, "b": 643.78018, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Table 4:", "bbox": {"l": 308.862, "t": 656.86136, "r": 341.73862, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Results of structure with content retrieved using", "bbox": {"l": 349.55927, "t": 656.86136, "r": 545.11517, "b": 665.76792, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "cell detection on PubTabNet. In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554274082183838, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500409364700317, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": 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We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "table", "bbox": {"l": 308.4068603515625, "t": 247.87631225585938, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691730737686157, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. 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Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500409364700317, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"id": 53, "text": "Model", "bbox": {"l": 78.843002, "t": 420.69037, "r": 104.85535, "b": 429.59692, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "TEDS", "bbox": {"l": 211.2, "t": 414.71237, "r": 236.10649, "b": 423.61893, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Dataset", "bbox": {"l": 129.338, "t": 426.66736, "r": 159.21584, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Simple", "bbox": {"l": 171.17096, "t": 426.66736, "r": 199.40497, "b": 435.57391000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Complex", "bbox": {"l": 211.36009, "t": 426.66736, "r": 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209173440933228, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433312892913818, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854635000228882, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4068603515625, "t": 247.87631225585938, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691730737686157, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, "t": 270.62134000000003, "r": 377.00076, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "PubTabNet", "bbox": {"l": 393.69809, "t": 270.62134000000003, "r": 438.28073, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "79.2", "bbox": {"l": 455.63559, "t": 270.62134000000003, "r": 473.07013, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "82.7", "bbox": {"l": 498.16592, "t": 270.62134000000003, "r": 515.60046, "b": 279.52788999999996, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "TableFormer", "bbox": {"l": 326.79501, "t": 282.57631999999995, "r": 377.86331, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "PubTabNet", "bbox": {"l": 393.69388, "t": 282.57631999999995, "r": 438.27652, "b": 291.48288, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "82.1", "bbox": {"l": 455.63101, "t": 282.45676, "r": 473.06555000000003, "b": 291.41315, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "86.8", 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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In all cases the input is PDF", "bbox": {"l": 308.862, "t": 668.81636, "r": 545.11505, "b": 677.7229199999999, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "documents with cropped tables.", "bbox": {"l": 308.862, "t": 680.77136, "r": 435.03836, "b": 689.6779300000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables."}, {"label": "page_footer", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}, "confidence": 0.8787972331047058, "cells": [{"id": 216, "text": "7", "bbox": {"l": 295.121, "t": 734.133358, "r": 300.10229, "b": 743.039921, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}], "body": [{"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}, "confidence": 0.9554274082183838, "cells": [{"id": 0, "text": "5.3.", "bbox": {"l": 50.112, "t": 74.40137000000016, "r": 63.704811, "b": 84.25342, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Datasets and Metrics", "bbox": {"l": 72.766685, "t": 74.40137000000016, "r": 167.89825, "b": 84.25342, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.35039999999992, "r": 286.36511, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"id": 2, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "bbox": {"l": 62.067001, "t": 93.35039999999992, "r": 286.36499, "b": 102.25696000000016, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ric was introduced in [37]. It represents the prediction, and", "bbox": {"l": 50.112, "t": 105.30542000000003, "r": 286.36511, "b": 114.21198000000015, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ground-truth as a tree structure of HTML tags. This simi-", "bbox": {"l": 50.112, "t": 117.26044000000002, "r": 286.36505, "b": 126.16699000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "larity is calculated as:", "bbox": {"l": 50.112, "t": 129.21642999999995, "r": 136.71687, "b": 138.12298999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:"}, {"label": "formula", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "formula", "bbox": {"l": 86.218994, "t": 150.31799, "r": 286.3624, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9500409364700317, "cells": [{"id": 6, "text": "TEDS (", "bbox": {"l": 86.218994, "t": 157.05798000000004, "r": 118.8784, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 118.87499, "t": 157.05798000000004, "r": 143.26962, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ") = 1", "bbox": {"l": 143.76799, "t": 157.05798000000004, "r": 165.9019, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "\u2212", "bbox": {"l": 168.12099, "t": 156.50012000000004, "r": 175.8699, "b": 165.90479000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "EditDist (", "bbox": {"l": 179.27899, "t": 150.31799, "r": 221.95677, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "T$_{a}$, T$_{b}$", "bbox": {"l": 221.95200000000003, "t": 150.31799, "r": 246.34663, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 246.84499999999997, "t": 150.31799, "r": 250.71945, "b": 159.16479000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "max (", "bbox": {"l": 182.21201, "t": 163.89197000000001, "r": 206.29161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "|", "bbox": {"l": 206.289, "t": 163.33411, "r": 209.05661, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "T$_{a}$", "bbox": {"l": 209.056, "t": 163.89197000000001, "r": 219.19968, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "|", "bbox": {"l": 219.69700999999998, "t": 163.33411, "r": 222.46461000000002, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": ",", "bbox": {"l": 224.125, "t": 163.89197000000001, "r": 226.89261, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "|", "bbox": {"l": 228.55299000000002, "t": 163.33411, "r": 231.3206, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "T$_{b}$", "bbox": {"l": 231.31999, "t": 163.89197000000001, "r": 240.64563, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "|", "bbox": {"l": 241.144, "t": 163.33411, "r": 243.91161, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": ")", "bbox": {"l": 243.911, "t": 163.89197000000001, "r": 247.78545, "b": 172.73877000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "(3)", "bbox": {"l": 274.746, "t": 157.21740999999997, "r": 286.3624, "b": 166.12396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TEDS ( T$_{a}$, T$_{b}$ ) = 1 \u2212 EditDist ( T$_{a}$, T$_{b}$ ) max ( | T$_{a}$ | , | T$_{b}$ | ) (3)"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.111984, "t": 181.00298999999995, "r": 286.36285, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9735319018363953, "cells": [{"id": 23, "text": "where", "bbox": {"l": 62.067001, "t": 181.16241000000002, "r": 86.405632, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "T$_{a}$", "bbox": {"l": 88.581001, "t": 181.00298999999995, "r": 98.724663, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and", "bbox": {"l": 101.399, "t": 181.16241000000002, "r": 115.785, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "T$_{b}$", "bbox": {"l": 117.961, "t": 181.00298999999995, "r": 127.28664, "b": 189.84978999999998, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "represent tables in tree structure HTML", "bbox": {"l": 129.95999, "t": 181.16241000000002, "r": 286.36285, "b": 190.06897000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "format. EditDist denotes the tree-edit distance, and", "bbox": {"l": 50.111992, "t": 193.11743, "r": 252.78116000000003, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "|", "bbox": {"l": 255.18201, "t": 192.40015000000005, "r": 257.94962, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "T", "bbox": {"l": 257.94901, "t": 192.95800999999994, "r": 263.77115, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "|", "bbox": {"l": 265.155, "t": 192.40015000000005, "r": 267.92261, "b": 201.80480999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "rep-", "bbox": {"l": 270.32199, "t": 193.11743, "r": 286.36179, "b": 202.02399000000003, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "resents the number of nodes in", "bbox": {"l": 50.111984, "t": 205.07245, "r": 172.13388, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "T", "bbox": {"l": 174.62399, "t": 204.91301999999996, "r": 180.44614, "b": 213.75982999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": ".", "bbox": {"l": 181.82899, "t": 205.07245, "r": 184.31964, "b": 213.97900000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"id": 36, "text": "5.4.", "bbox": {"l": 50.112, "t": 224.81946000000005, "r": 64.551605, "b": 234.67151, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Quantitative Analysis", "bbox": {"l": 74.178009, "t": 224.81946000000005, "r": 170.45169, "b": 234.67151, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.6499, "r": 286.36514, "b": 396.13794, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"id": 38, "text": "Structure.", "bbox": {"l": 62.067001, "t": 243.6499, "r": 105.32461, "b": 252.60626000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "As shown in Tab.", "bbox": {"l": 112.12600000000002, "t": 243.76946999999996, "r": 184.68361, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2, TableFormer outper-", "bbox": {"l": 191.4781, "t": 243.76946999999996, "r": 286.36188, "b": 252.67602999999997, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "forms all SOTA methods across different datasets by a large", "bbox": {"l": 50.112, "t": 255.72448999999995, "r": 286.36508, "b": 264.63104, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "margin for predicting the table structure from an image.", "bbox": {"l": 50.112, "t": 267.67949999999996, "r": 286.36508, "b": 276.58606, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "All the more, our model outperforms pre-trained methods.", "bbox": {"l": 50.112, "t": 279.63446, "r": 286.36508, "b": 288.54105, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "During the evaluation we do not apply any table filtering.", "bbox": {"l": 50.112, "t": 291.59048, "r": 286.36514, "b": 300.49704, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "We also provide our baseline results on the SynthTabNet", "bbox": {"l": 50.112, "t": 303.54547, "r": 286.36508, "b": 312.45203000000004, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "dataset. It has been observed that large tables (e.g. tables", "bbox": {"l": 50.112, "t": 315.50046, "r": 286.36505, "b": 324.40700999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "that occupy half of the page or more) yield poor predictions.", "bbox": {"l": 50.112, "t": 327.45544, "r": 286.36508, "b": 336.362, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "We attribute this issue to the image resizing during the pre-", "bbox": {"l": 50.112, "t": 339.41043, "r": 286.36508, "b": 348.31699000000003, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "processing step, that produces downsampled images with", "bbox": {"l": 50.112, "t": 351.36542, "r": 286.36505, "b": 360.27197, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "indistinguishable features. This problem can be addressed", "bbox": {"l": 50.112, "t": 363.32141, "r": 286.36508, "b": 372.2279700000001, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "by treating such big tables with a separate model which ac-", "bbox": {"l": 50.112, "t": 375.2764, "r": 286.36511, "b": 384.18295000000006, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cepts a large input image size.", "bbox": {"l": 50.112, "t": 387.23138, "r": 170.01187, "b": 396.13794, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. 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"text": "96.7", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 15, "page_no": 6, "cluster": {"id": 15, "label": "text", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 613.29492, "coord_origin": "TOPLEFT"}, "confidence": 0.7209173440933228, "cells": [{"id": 109, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet", "bbox": {"l": 50.112, "t": 592.43336, "r": 286.36511, "b": 601.33992, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "(FTN), TableBank (TB) and SynthTabNet (STN).", "bbox": {"l": 50.112, "t": 604.38837, "r": 247.46114, "b": 613.29492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN)."}, {"label": "text", "id": 16, "page_no": 6, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}, "confidence": 0.6433312892913818, "cells": [{"id": 111, "text": "FT: Model was trained on PubTabNet then finetuned.", "bbox": {"l": 50.112, "t": 616.34337, "r": 261.78732, "b": 625.24992, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "FT: Model was trained on PubTabNet then finetuned."}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112015, "t": 644.3498099999999, "r": 286.366, "b": 713.151932, "coord_origin": "TOPLEFT"}, "confidence": 0.9854635000228882, "cells": [{"id": 112, "text": "Cell Detection.", "bbox": {"l": 62.067001, "t": 644.3498099999999, "r": 124.72179, "b": 653.30618, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Like any object detector, our", "bbox": {"l": 128.20401, "t": 644.46936, "r": 242.9333, "b": 653.37592, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "Cell BBox", "bbox": {"l": 245.55401999999998, "t": 644.55902, "r": 286.36084, "b": 653.1467700000001, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Detector", "bbox": {"l": 50.112015, "t": 656.51402, "r": 84.971146, "b": 665.10178, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "provides bounding boxes that can be improved", "bbox": {"l": 89.515015, "t": 656.42436, "r": 286.366, "b": 665.33092, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "with post-processing during inference. We make use of the", "bbox": {"l": 50.112015, "t": 668.37936, "r": 286.36511, "b": 677.28593, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "grid-like structure of tables to refine the predictions. A de-", "bbox": {"l": 50.112015, "t": 680.33536, "r": 286.36505, "b": 689.24193, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "tailed explanation on the post-processing is available in the", "bbox": {"l": 50.112015, "t": 692.290359, "r": 286.36511, "b": 701.19693, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "supplementary material. As shown in Tab. 3, we evaluate", "bbox": {"l": 50.112015, "t": 704.245361, "r": 286.36508, "b": 713.151932, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 545.11517, "b": 227.57709, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"id": 121, "text": "our", "bbox": {"l": 308.862, "t": 75.20836999999995, "r": 322.14215, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Cell BBox Decoder", "bbox": {"l": 325.45401, "t": 75.29803000000004, "r": 404.56702, "b": 83.88580000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "accuracy for cells with a class la-", "bbox": {"l": 408.104, "t": 75.20836999999995, "r": 545.10968, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "bel of \u2018content\u2019 only using the PASCAL VOC mAP metric", "bbox": {"l": 308.862, "t": 87.16339000000005, "r": 545.11511, "b": 96.06994999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "for pre-processing and post-processing.", "bbox": {"l": 308.862, "t": 99.11841000000004, "r": 470.22626, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Note that we do", "bbox": {"l": 477.52884, "t": 99.11841000000004, "r": 545.11511, "b": 108.02495999999985, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "not have post-processing results for SynthTabNet as images", "bbox": {"l": 308.862, "t": 111.07343000000003, "r": 545.11517, "b": 119.97997999999984, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "are only provided. To compare the performance of our pro-", "bbox": {"l": 308.862, "t": 123.02844000000005, "r": 545.11511, "b": 131.93499999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "posed approach, we\u2019ve integrated TableFormer\u2019s", "bbox": {"l": 308.862, "t": 134.98443999999995, "r": 502.01691000000005, "b": 143.89099, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "Cell BBox", "bbox": {"l": 504.47299, "t": 135.07410000000004, "r": 545.11041, "b": 143.66187000000002, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Decoder", "bbox": {"l": 308.862, "t": 147.02910999999995, "r": 343.16324, "b": 155.61688000000004, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "into EDD architecture. As mentioned previously,", "bbox": {"l": 346.371, "t": 146.93944999999997, "r": 545.11493, "b": 155.84600999999998, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "the Structure Decoder provides the", "bbox": {"l": 308.862, "t": 158.89446999999996, "r": 446.15652, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "Cell BBox Decoder", "bbox": {"l": 448.28998000000007, "t": 158.98413000000005, "r": 525.04181, "b": 167.57190000000003, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "with", "bbox": {"l": 527.39899, "t": 158.89446999999996, "r": 545.11249, "b": 167.80102999999997, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "the features needed to predict the bounding box predictions.", "bbox": {"l": 308.862, "t": 170.84948999999995, "r": 545.11511, "b": 179.75603999999998, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Therefore, the accuracy of the", "bbox": {"l": 308.862, "t": 182.80449999999996, "r": 432.86642000000006, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "Structure Decoder", "bbox": {"l": 436.39001, "t": 182.89417000000003, "r": 510.93021, "b": 191.48193000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "directly", "bbox": {"l": 514.677, "t": 182.80449999999996, "r": 545.11273, "b": 191.71105999999997, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "influences the accuracy of the", "bbox": {"l": 308.862, "t": 194.75951999999995, "r": 431.17285, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Cell BBox Decoder", "bbox": {"l": 434.6790199999999, "t": 194.84918000000005, "r": 514.18054, "b": 203.43695000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ". If the", "bbox": {"l": 514.17603, "t": 194.75951999999995, "r": 545.10992, "b": 203.66607999999997, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Structure Decoder", "bbox": {"l": 308.86203, "t": 206.80517999999995, "r": 382.35614, "b": 215.39293999999995, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "predicts an extra column, this will result", "bbox": {"l": 385.07501, "t": 206.71551999999997, "r": 545.11426, "b": 215.62207, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "in an extra column of predicted bounding boxes.", "bbox": {"l": 308.862, "t": 218.67052999999999, "r": 501.6981799999999, "b": 227.57709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "our Cell BBox Decoder accuracy for cells with a class label of \u2018content\u2019 only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we\u2019ve integrated TableFormer\u2019s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes."}, {"label": "table", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "table", "bbox": {"l": 308.4068603515625, "t": 247.87631225585938, "r": 533.6419677734375, "b": 303.8056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9691730737686157, "cells": [{"id": 146, "text": "Model", "bbox": {"l": 339.323, "t": 253.66436999999996, "r": 365.33536, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Dataset", "bbox": {"l": 401.04132, "t": 253.66436999999996, "r": 430.91916, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "mAP", "bbox": {"l": 454.10214, "t": 253.66436999999996, "r": 474.58523999999994, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "mAP (PP)", "bbox": {"l": 486.54034, "t": 253.66436999999996, "r": 527.2276, "b": 262.57092, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "EDD+BBox", "bbox": {"l": 327.65601, 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PP: Post-processing.", "bbox": {"l": 308.862, "t": 328.4043, "r": 474.97845, "b": 337.3108500000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.86197, "t": 367.6797199999999, "r": 545.11566, "b": 520.16769, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"id": 165, "text": "Cell Content.", "bbox": {"l": 320.81699, "t": 367.6797199999999, "r": 378.94876, "b": 376.63611, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "In this section, we evaluate the entire", "bbox": {"l": 387.07898, "t": 367.79929, "r": 545.11566, "b": 376.70584, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "pipeline of recovering a table with content.", "bbox": {"l": 308.86197, "t": 379.75426999999996, "r": 487.19257, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Here we put", "bbox": {"l": 493.96713, "t": 379.75426999999996, "r": 545.11511, "b": 388.66083, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "our approach to test by capitalizing on extracting content", "bbox": {"l": 308.86197, "t": 391.70926, "r": 545.11505, "b": 400.61581, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "from the PDF cells rather than decoding from images. Tab.", "bbox": {"l": 308.86197, "t": 403.66525, "r": 545.11523, "b": 412.57181, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "4", "bbox": {"l": 308.86197, "t": 415.62024, "r": 314.08096, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "shows the TEDs score of HTML code representing the", "bbox": {"l": 316.69046, "t": 415.62024, "r": 545.11517, "b": 424.52679, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "structure of the table along with the content inserted in the", "bbox": {"l": 308.86197, "t": 427.57523, "r": 545.11505, "b": 436.48177999999996, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "data cell and compared with the ground-truth. Our method", "bbox": {"l": 308.86197, "t": 439.53021, "r": 545.11505, "b": 448.43677, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "achieved a", "bbox": {"l": 308.86197, "t": 451.4852, "r": 350.23666, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "5.3%", "bbox": {"l": 352.17596, "t": 451.36563, "r": 374.59183, "b": 460.32201999999995, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "increase over the state-of-the-art, and com-", "bbox": {"l": 376.53296, "t": 451.4852, "r": 545.11011, "b": 460.39175, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "mercial solutions. We believe our scores would be higher", "bbox": {"l": 308.86197, "t": 463.44019, "r": 545.11511, "b": 472.34674, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "if the HTML ground-truth matched the extracted PDF cell", "bbox": {"l": 308.86197, "t": 475.39618, "r": 545.11517, "b": 484.30273, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "content. Unfortunately, there are small discrepancies such", "bbox": {"l": 308.86197, "t": 487.35117, "r": 545.11511, "b": 496.25772, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "as spacings around words or special characters with various", "bbox": {"l": 308.86197, "t": 499.30615, "r": 545.11505, "b": 508.21271, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "unicode representations.", "bbox": {"l": 308.86197, "t": 511.26114, "r": 405.69846, "b": 520.16769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"id": 183, "text": "Model", "bbox": {"l": 358.01099, "t": 552.23337, "r": 384.02335, "b": 561.1399200000001, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "TEDS", "bbox": {"l": 449.03400000000005, "t": 546.25537, "r": 473.94049000000007, "b": 555.16193, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "Simple", "bbox": {"l": 408.50598, "t": 558.21037, "r": 436.73999, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "Complex", "bbox": {"l": 448.6951, "t": 558.21037, "r": 485.07849, "b": 567.11693, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "All", "bbox": {"l": 499.3848, 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"TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": 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different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. 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Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "bbox": {"l": 328.78101, "t": 704.920792, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}, "confidence": 0.8709858059883118, "cells": [{"id": 220, "text": "8", "bbox": {"l": 295.121, "t": 734.133366, "r": 300.10229, "b": 743.039928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5836486816406, "t": 98.65415954589844, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699173092842102, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.5789337158203, "coord_origin": "TOPLEFT"}, "confidence": 0.7873926162719727, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"id": 0, "text": "b.", "bbox": {"l": 53.811783000000005, "t": 208.23328000000004, "r": 62.219952, "b": 216.10645, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Structure predicted by TableFormer, with superimposed matched PDF cell text:", "bbox": {"l": 66.424026, "t": 208.23328000000004, "r": 385.93451, "b": 216.10645, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"id": 2, "text": "Japanese language (previously unseen by TableFormer):", "bbox": {"l": 53.811783000000005, "t": 94.28112999999996, "r": 284.34592, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.83081, "t": 94.28112999999996, "r": 431.09119, "b": 102.15430000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"id": 3, "text": "Example table from FinTabNet:", "bbox": {"l": 304.83081, "t": 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"label": "caption", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}, "confidence": 0.9153602719306946, "cells": [{"id": 181, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "bbox": {"l": 62.595001, "t": 458.72836, "r": 532.63049, "b": 467.63492, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"id": 182, "text": "5.5.", "bbox": {"l": 50.112, "t": 491.39536, "r": 64.448898, "b": 501.24741, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "Qualitative Analysis", "bbox": {"l": 74.006828, "t": 491.39536, "r": 163.7558, "b": 501.24741, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 536.87337, "r": 286.36511, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, "cells": [{"id": 184, "text": "We showcase several visualizations for the different", "bbox": {"l": 62.067001, "t": 536.87337, "r": 286.36499, "b": 545.77992, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "components of our network on various", "bbox": {"l": 50.112, "t": 548.82837, "r": 211.15741, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "\u201ccomplex\u201d", "bbox": {"l": 215.10000999999997, "t": 548.91803, "r": 259.17453, "b": 557.50578, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "tables", "bbox": {"l": 263.12, "t": 548.82837, "r": 286.36273, "b": 557.73492, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "within datasets presented in this work in Fig. 5 and Fig. 6", "bbox": {"l": 50.112, "t": 560.78337, "r": 286.36505, "b": 569.68993, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "As it is shown, our model is able to predict bounding boxes", "bbox": {"l": 50.112, "t": 572.73837, "r": 286.36508, "b": 581.6449299999999, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "for all table cells, even for the empty ones. Additionally,", "bbox": {"l": 50.112, "t": 584.69337, "r": 286.36508, "b": 593.59993, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "our post-processing techniques can extract the cell content", "bbox": {"l": 50.112, "t": 596.64937, "r": 286.36505, "b": 605.55592, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "by matching the predicted bounding boxes to the PDF cells", "bbox": {"l": 50.112, "t": 608.60437, "r": 286.36508, "b": 617.51093, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "based on their overlap and spatial proximity. The left part", "bbox": {"l": 50.112, "t": 620.55937, "r": 286.36508, "b": 629.46593, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "of Fig. 5 demonstrates also the adaptability of our method", "bbox": {"l": 50.112, "t": 632.51437, "r": 286.36508, "b": 641.42093, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "to any language, as it can successfully extract Japanese", "bbox": {"l": 50.112, "t": 644.46938, "r": 286.36508, "b": 653.37593, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "text, although the training set contains only English content.", "bbox": {"l": 50.112, "t": 656.42438, "r": 286.36511, "b": 665.33094, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "We provide more visualizations including the intermediate", "bbox": {"l": 50.112, "t": 668.38037, "r": 286.36508, "b": 677.28694, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "steps in the supplementary material. Overall these illustra-", "bbox": {"l": 50.112, "t": 680.33537, "r": 286.36511, "b": 689.24194, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "tions justify the versatility of our method across a diverse", "bbox": {"l": 50.112, "t": 692.290375, "r": 286.36511, "b": 701.196945, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "range of table appearances and content type.", "bbox": {"l": 50.112, "t": 704.245377, "r": 226.88833999999997, "b": 713.1519470000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various \u201ccomplex\u201d tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"id": 201, "text": "6.", "bbox": {"l": 308.862, "t": 490.70892, "r": 316.07382, "b": 501.45663, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Future Work & Conclusion", "bbox": {"l": 325.68954, "t": 490.70892, "r": 460.84848, "b": 501.45663, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 512.89337, "r": 545.11517, "b": 653.30592, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"id": 203, "text": "In this paper, we presented TableFormer an end-to-end", "bbox": {"l": 320.81699, "t": 512.89337, "r": 545.11505, "b": 521.79993, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "transformer based approach to predict table structures and", "bbox": {"l": 308.862, "t": 524.84836, "r": 545.11517, "b": 533.75491, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "bounding boxes of cells from an image. This approach en-", "bbox": {"l": 308.862, "t": 536.80336, "r": 545.11511, "b": 545.70992, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "ables us to recreate the table structure, and extract the cell", "bbox": {"l": 308.862, "t": 548.75836, "r": 545.11505, "b": 557.6649199999999, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "content from PDF or OCR by using bounding boxes. Ad-", "bbox": {"l": 308.862, "t": 560.71336, "r": 545.11517, "b": 569.61992, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "ditionally, it provides the versatility required in real-world", "bbox": {"l": 308.862, "t": 572.66837, "r": 545.11511, "b": 581.57492, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "scenarios when dealing with various types of PDF docu-", "bbox": {"l": 308.862, "t": 584.62436, "r": 545.11511, "b": 593.53091, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "ments, and languages.", "bbox": {"l": 308.862, "t": 596.57936, "r": 400.46808, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Furthermore, our method outper-", "bbox": {"l": 408.37839, "t": 596.57936, "r": 545.11511, "b": 605.48592, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "forms all state-of-the-arts with a wide margin. Finally, we", "bbox": {"l": 308.862, "t": 608.53436, "r": 545.11505, "b": 617.44092, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "introduce \u201cSynthTabNet\u201d a challenging synthetically gen-", "bbox": {"l": 308.862, "t": 620.48936, "r": 545.11511, "b": 629.3959199999999, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "erated dataset that reinforces missing characteristics from", "bbox": {"l": 308.862, "t": 632.4443699999999, "r": 545.11505, "b": 641.35092, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "other datasets.", "bbox": {"l": 308.862, "t": 644.39937, "r": 365.85803, "b": 653.30592, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \u201cSynthTabNet\u201d a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"id": 216, "text": "References", "bbox": {"l": 308.862, "t": 672.09892, "r": 364.40585, "b": 682.84664, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 545.1134, "b": 712.936752, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"id": 217, "text": "[1]", "bbox": {"l": 313.345, "t": 693.9617920000001, "r": 323.80792, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "bbox": {"l": 326.05127, "t": 693.9617920000001, "r": 545.10852, "b": 701.977753, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}, "confidence": 0.9069584012031555, "cells": [{"id": 127, "text": "10", "bbox": {"l": 292.63, "t": 734.1329920000001, "r": 302.59259, "b": 743.039555, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"id": 0, "text": "TableFormer: Table Structure Understanding with Transformers", "bbox": {"l": 132.842, "t": 110.57488999999998, "r": 465.37591999999995, "b": 121.32263, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Supplementary Material", "bbox": {"l": 220.18399, "t": 122.25982999999997, "r": 375.04269, "b": 135.53008999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.111984, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"id": 2, "text": "1.", "bbox": {"l": 50.111984, "t": 161.16089, "r": 57.089828, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Details on the datasets", "bbox": {"l": 66.393616, "t": 161.16089, "r": 175.96437, "b": 171.90863000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"id": 4, "text": "1.1.", "bbox": {"l": 50.111984, "t": 180.97931000000005, "r": 64.210808, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Data preparation", "bbox": {"l": 73.610023, "t": 180.97931000000005, "r": 150.36401, "b": 190.83136000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.111984, "t": 199.92029000000002, "r": 286.36514, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"id": 6, "text": "As a first step of our data preparation process, we have", "bbox": {"l": 62.06698600000001, "t": 199.92029000000002, "r": 286.36496, "b": 208.82683999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "calculated statistics over the datasets across the following", "bbox": {"l": 50.111984, "t": 211.87627999999995, "r": 286.36505, "b": 220.78283999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "dimensions: (1) table size measured in the number of rows", "bbox": {"l": 50.111984, "t": 223.83130000000006, "r": 286.36514, "b": 232.73784999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "and columns, (2) complexity of the table, (3) strictness of", "bbox": {"l": 50.111984, "t": 235.78632000000005, "r": 286.36508, "b": 244.69286999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the provided HTML structure and (4) completeness (i.e. no", "bbox": {"l": 50.111984, "t": 247.74132999999995, "r": 286.36505, "b": 256.64788999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "omitted bounding boxes). A table is considered to be simple", "bbox": {"l": 50.111984, "t": 259.69635000000005, "r": 286.36505, "b": 268.60290999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "if it does not contain row spans or column spans. Addition-", "bbox": {"l": 50.111984, "t": 271.65137000000004, "r": 286.36505, "b": 280.55792, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ally, a table has a strict HTML structure if every row has the", "bbox": {"l": 50.111984, "t": 283.60736, "r": 286.36502, "b": 292.5139199999999, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "same number of columns after taking into account any row", "bbox": {"l": 50.111984, "t": 295.56235, "r": 286.36505, "b": 304.4689, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "or column spans. Therefore a strict HTML structure looks", "bbox": {"l": 50.111984, "t": 307.5173300000001, "r": 286.36508, "b": 316.42389, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "always rectangular. However, HTML is a lenient encoding", "bbox": {"l": 50.111984, "t": 319.47232, "r": 286.36505, "b": 328.3788799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "format, i.e. tables with rows of different sizes might still", "bbox": {"l": 50.111984, "t": 331.42731000000003, "r": 286.36502, "b": 340.33386, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "be regarded as correct due to implicit display rules. These", "bbox": {"l": 50.111984, "t": 343.3833, "r": 286.36508, "b": 352.28986, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implicit rules leave room for ambiguity, which we want to", "bbox": {"l": 50.111984, "t": 355.33829, "r": 286.36505, "b": 364.24484000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables", "bbox": {"l": 50.111984, "t": 367.29327, "r": 286.36508, "b": 376.19983, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "where every row has exactly the same length.", "bbox": {"l": 50.111984, "t": 379.24826, "r": 230.80364999999998, "b": 388.15482000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.111984, "t": 391.40527, "r": 286.36511, "b": 627.4597, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"id": 22, "text": "We have developed a technique that tries to derive a", "bbox": {"l": 62.06698600000001, "t": 391.40527, "r": 286.36499, "b": 400.31183, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "missing bounding box out of its neighbors. As a first step,", "bbox": {"l": 50.111984, "t": 403.36026, "r": 286.36508, "b": 412.26681999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "we use the annotation data to generate the most fine-grained", "bbox": {"l": 50.111984, "t": 415.31525, "r": 286.36505, "b": 424.22180000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "grid that covers the table structure. In case of strict HTML", "bbox": {"l": 50.111984, "t": 427.2712399999999, "r": 286.36505, "b": 436.1778, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tables, all grid squares are associated with some table cell", "bbox": {"l": 50.111984, "t": 439.22623, "r": 286.36508, "b": 448.1327800000001, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "and in the presence of table spans a cell extends across mul-", "bbox": {"l": 50.111984, "t": 451.18121, "r": 286.36511, "b": 460.08777, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "tiple grid squares. When enough bounding boxes are known", "bbox": {"l": 50.111984, "t": 463.1362, "r": 286.36505, "b": 472.04276, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "for a rectangular table, it is possible to compute the geo-", "bbox": {"l": 50.111984, "t": 475.09119, "r": 286.36508, "b": 483.99774, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "metrical border lines between the grid rows and columns.", "bbox": {"l": 50.111984, "t": 487.04617, "r": 286.36502, "b": 495.95273, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Eventually this information is used to generate the missing", "bbox": {"l": 50.111984, "t": 499.00217, "r": 286.36511, "b": 507.90872, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "bounding boxes. Additionally, the existence of unused grid", "bbox": {"l": 50.111984, "t": 510.95715, "r": 286.36508, "b": 519.8637100000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "squares indicates that the table rows have unequal number", "bbox": {"l": 50.111984, "t": 522.91214, "r": 286.36508, "b": 531.8187, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "of columns and the overall structure is non-strict. The gen-", "bbox": {"l": 50.111984, "t": 534.86713, "r": 286.36505, "b": 543.7737, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "eration of missing bounding boxes for non-strict HTML ta-", "bbox": {"l": 50.111984, "t": 546.82214, "r": 286.36502, "b": 555.7287, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "bles is ambiguous and therefore quite challenging.", "bbox": {"l": 50.111984, "t": 558.77814, "r": 257.47351, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Thus,", "bbox": {"l": 263.94919, "t": 558.77814, "r": 286.36505, "b": 567.68469, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "we have decided to simply discard those tables. In case of", "bbox": {"l": 50.111984, "t": 570.73314, "r": 286.36508, "b": 579.63969, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "PubTabNet we have computed missing bounding boxes for", "bbox": {"l": 50.111984, "t": 582.68814, "r": 286.36511, "b": 591.5947, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "48% of the simple and 69% of the complex tables. Regard-", "bbox": {"l": 50.111984, "t": 594.64314, "r": 286.36511, "b": 603.5497, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "bbox": {"l": 50.111984, "t": 606.5981400000001, "r": 286.36505, "b": 615.5047, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "tables require the generation of bounding boxes.", "bbox": {"l": 50.111984, "t": 618.55315, "r": 242.2606, "b": 627.4597, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.111984, "t": 630.71014, "r": 286.36496, "b": 651.57269, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"id": 43, "text": "Figure 7 illustrates the distribution of the tables across", "bbox": {"l": 62.06698600000001, "t": 630.71014, "r": 286.36496, "b": 639.6167, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "different dimensions per dataset.", "bbox": {"l": 50.111984, "t": 642.66614, "r": 179.90472, "b": 651.57269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.111984, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"id": 45, "text": "1.2.", "bbox": {"l": 50.111984, "t": 662.39014, "r": 64.297272, "b": 672.24219, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Synthetic datasets", "bbox": {"l": 73.754135, "t": 662.39014, "r": 153.60785, "b": 672.24219, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.111984, "t": 681.33113, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"id": 47, "text": "Aiming to train and evaluate our models in a broader", "bbox": {"l": 62.06698600000001, "t": 681.33113, "r": 286.36493, "b": 690.2377, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "spectrum of table data we have synthesized four types of", "bbox": {"l": 50.111984, "t": 693.2861330000001, "r": 286.36505, "b": 702.1927029999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "datasets.", "bbox": {"l": 50.111984, "t": 705.241135, "r": 84.144226, "b": 714.147705, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Each one contains tables with different appear-", "bbox": {"l": 91.237595, "t": 705.241135, "r": 286.36505, "b": 714.147705, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 207.42773, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"id": 51, "text": "ances in regard to their size, structure, style and content.", "bbox": {"l": 308.862, "t": 162.65515000000005, "r": 545.11511, "b": 171.56170999999995, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Every synthetic dataset contains 150k examples, summing", "bbox": {"l": 308.862, "t": 174.61017000000004, "r": 545.11511, "b": 183.51671999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "up to 600k synthetic examples. All datasets are divided into", "bbox": {"l": 308.862, "t": 186.56519000000003, "r": 545.11511, "b": 195.47173999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Train, Test and Val splits (80%, 10%, 10%).", "bbox": {"l": 308.862, "t": 198.52117999999996, "r": 484.07434, "b": 207.42773, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.23517000000004, "r": 545.11505, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"id": 55, "text": "The process of generating a synthetic dataset can be de-", "bbox": {"l": 320.81699, "t": 211.23517000000004, "r": 545.11505, "b": 220.14171999999996, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "composed into the following steps:", "bbox": {"l": 308.862, "t": 223.19019000000003, "r": 448.08939, "b": 232.09673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 235.90521, "r": 545.11511, "b": 316.54279, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"id": 57, "text": "1.", "bbox": {"l": 320.81699, "t": 235.90521, "r": 328.28894, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Prepare styling and content templates: The styling", "bbox": {"l": 335.38232, "t": 235.90521, "r": 545.11499, "b": 244.81177000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "templates have been manually designed and organized into", "bbox": {"l": 308.862, "t": 247.86023, "r": 545.11511, "b": 256.76678000000004, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "groups of scope specific appearances (e.g. financial data,", "bbox": {"l": 308.862, "t": 259.81525, "r": 545.11511, "b": 268.72180000000003, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "marketing data, etc.)", "bbox": {"l": 308.862, "t": 271.77026, "r": 393.3847, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Additionally, we have prepared cu-", "bbox": {"l": 400.11942, "t": 271.77026, "r": 545.11511, "b": 280.67682, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "rated collections of content templates by extracting the most", "bbox": {"l": 308.862, "t": 283.72524999999996, "r": 545.11505, "b": 292.63181, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "frequently used terms out of non-synthetic datasets (e.g.", "bbox": {"l": 308.862, "t": 295.68124, "r": 545.11511, "b": 304.5878000000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "PubTabNet, FinTabNet, etc.).", "bbox": {"l": 308.862, "t": 307.63623, "r": 425.69348, "b": 316.54279, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.35022, "r": 545.11517, "b": 448.80865, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"id": 66, "text": "2.", "bbox": {"l": 320.81699, "t": 320.35022, "r": 328.4949, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Generate table structures: The structure of each syn-", "bbox": {"l": 331.05423, "t": 320.35022, "r": 545.11499, "b": 329.25677, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "thetic dataset assumes a horizontal table header which po-", "bbox": {"l": 308.862, "t": 332.30521000000005, "r": 545.11517, "b": 341.21176, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tentially spans over multiple rows and a table body that", "bbox": {"l": 308.862, "t": 344.26018999999997, "r": 545.11505, "b": 353.16675, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "may contain a combination of row spans and column spans.", "bbox": {"l": 308.862, "t": 356.21619, "r": 545.11511, "b": 365.12273999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "However, spans are not allowed to cross the header - body", "bbox": {"l": 308.862, "t": 368.17117, "r": 545.11511, "b": 377.07773, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "boundary. The table structure is described by the parame-", "bbox": {"l": 308.862, "t": 380.12616, "r": 545.11499, "b": 389.03271, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ters: Total number of table rows and columns, number of", "bbox": {"l": 308.862, "t": 392.08115, "r": 545.11517, "b": 400.98769999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "header rows, type of spans (header only spans, row only", "bbox": {"l": 308.862, "t": 404.03613000000007, "r": 545.11511, "b": 412.94269, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "spans, column only spans, both row and column spans),", "bbox": {"l": 308.862, "t": 415.99112, "r": 545.11499, "b": 424.89767, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "maximum span size and the ratio of the table area covered", "bbox": {"l": 308.862, "t": 427.94711, "r": 545.11517, "b": 436.85367, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "by spans.", "bbox": {"l": 308.862, "t": 439.9021, "r": 345.94278, "b": 448.80865, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.61609, "r": 545.11517, "b": 497.38861, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"id": 78, "text": "3.", "bbox": {"l": 320.81699, "t": 452.61609, "r": 328.30341, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Generate content: Based on the dataset", "bbox": {"l": 330.79889, "t": 452.61609, "r": 485.75772000000006, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "theme", "bbox": {"l": 488.073, "t": 452.70575, "r": 511.86368, "b": 461.29352, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": ", a set of", "bbox": {"l": 511.86301, "t": 452.61609, "r": 545.10815, "b": 461.52264, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "suitable content templates is chosen first. Then, this content", "bbox": {"l": 308.862, "t": 464.57108, "r": 545.11505, "b": 473.47763, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "can be combined with purely random text to produce the", "bbox": {"l": 308.862, "t": 476.52707, "r": 545.11517, "b": 485.43362, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "synthetic content.", "bbox": {"l": 308.862, "t": 488.48206, "r": 379.14816, "b": 497.38861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.19604, "r": 545.1153, "b": 545.96858, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"id": 85, "text": "4.", "bbox": {"l": 320.81699, "t": 501.19604, "r": 328.66177, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Apply styling templates: Depending on the domain", "bbox": {"l": 331.2767, "t": 501.19604, "r": 545.11493, "b": 510.1026, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the synthetic dataset, a set of styling templates is first", "bbox": {"l": 308.862, "t": 513.15103, "r": 545.1153, "b": 522.05759, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "manually selected.", "bbox": {"l": 308.862, "t": 525.10703, "r": 384.29883, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Then, a style is randomly selected to", "bbox": {"l": 391.25272, "t": 525.10703, "r": 545.11511, "b": 534.01358, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "format the appearance of the synthesized table.", "bbox": {"l": 308.862, "t": 537.06203, "r": 496.15897000000007, "b": 545.96858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 549.77603, "r": 545.11517, "b": 606.50359, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"id": 91, "text": "5.", "bbox": {"l": 320.81699, "t": 549.77603, "r": 328.28894, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Render the complete tables: The synthetic table is", "bbox": {"l": 335.40222, "t": 549.77603, "r": 545.11499, "b": 558.68259, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "finally rendered by a web browser engine to generate the", "bbox": {"l": 308.862, "t": 561.73103, "r": 545.11517, "b": 570.63759, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "bounding boxes for each table cell. A batching technique is", "bbox": {"l": 308.862, "t": 573.68604, "r": 545.11511, "b": 582.59259, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "utilized to optimize the runtime overhead of the rendering", "bbox": {"l": 308.862, "t": 585.64203, "r": 545.11505, "b": 594.54858, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "process.", "bbox": {"l": 308.862, "t": 597.59703, "r": 341.2305, "b": 606.50359, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 545.10876, "b": 646.98631, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"id": 97, "text": "2.", "bbox": {"l": 308.862, "t": 622.2905900000001, "r": 316.76675, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "Prediction post-processing for PDF docu-", "bbox": {"l": 327.30643, "t": 622.2905900000001, "r": 545.10876, "b": 633.03831, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "ments", "bbox": {"l": 326.79501, "t": 636.2385899999999, "r": 357.34055, "b": 646.98631, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.86203, "t": 657.42104, "r": 545.11517, "b": 714.148605, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"id": 100, "text": "Although TableFormer can predict the table structure and", "bbox": {"l": 320.81702, "t": 657.42104, "r": 545.11499, "b": 666.3276, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "the bounding boxes for tables recognized inside PDF docu-", "bbox": {"l": 308.86203, "t": 669.37604, "r": 545.11511, "b": 678.2826, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "ments, this is not enough when a full reconstruction of the", "bbox": {"l": 308.86203, "t": 681.33104, "r": 545.11517, "b": 690.2376, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "original table is required. This happens mainly due the fol-", "bbox": {"l": 308.86203, "t": 693.286041, "r": 545.11505, "b": 702.1926040000001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "lowing reasons:", "bbox": {"l": 308.86203, "t": 705.242035, "r": 371.42719, "b": 714.148605, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"id": 105, "text": "11", "bbox": {"l": 292.63104, "t": 734.1330379999999, "r": 302.59363, "b": 743.0396, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Complex", "bbox": {"l": 287.89441, "t": 141.71063000000004, "r": 310.14572, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Non", "bbox": {"l": 289.23572, "t": 93.07977000000005, "r": 299.37451, "b": 98.03698999999995, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Strict", "bbox": {"l": 285.63513, "t": 99.19976999999994, "r": 299.3735, "b": 104.15698000000009, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "HTML", "bbox": {"l": 285.26111, "t": 105.31975999999997, "r": 299.37537, "b": 110.27697999999998, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Strict", "bbox": {"l": 285.43109, "t": 120.38995, "r": 299.16946, "b": 125.34717, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HTML", "bbox": {"l": 285.05713, "t": 126.50995, "r": 299.17139, "b": 131.46716000000004, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Simple", "bbox": {"l": 311.34592, "t": 141.71063000000004, "r": 328.80933, "b": 146.66785000000004, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "47K", "bbox": {"l": 299.58362, "t": 86.69353999999998, "r": 309.60205, "b": 91.65075999999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Simple", "bbox": {"l": 466.04077000000007, "t": 141.67169, "r": 483.50418, "b": 146.62891000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Non", "bbox": {"l": 459.02151, "t": 93.76116999999999, "r": 469.16031000000004, "b": 98.71838000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Strict", "bbox": {"l": 455.4209, "t": 99.88116000000002, "r": 469.15927000000005, "b": 104.83838000000003, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "HTML", "bbox": {"l": 455.04691, "t": 106.00116000000014, "r": 469.16115999999994, "b": 110.95836999999995, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "145K", "bbox": {"l": 467.39401, "t": 85.57239000000004, "r": 480.6545100000001, "b": 90.52959999999996, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Complex", "bbox": {"l": 160.37672, "t": 141.58385999999996, "r": 182.62802, "b": 146.54107999999997, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Contain", "bbox": {"l": 153.74265, "t": 94.86481000000003, "r": 173.32664, "b": 99.82201999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Missing", "bbox": {"l": 154.50967, "t": 100.98479999999995, "r": 173.3246, "b": 105.94202000000007, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "bboxes", "bbox": {"l": 155.27162, "t": 107.10479999999995, "r": 173.32664, "b": 112.06200999999987, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Contain", "bbox": {"l": 326.41302, "t": 107.23248000000001, "r": 345.99701, "b": 112.18970000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Missing", "bbox": {"l": 327.17972, "t": 113.35248000000001, "r": 345.99463, "b": 118.30969000000005, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "bboxes", "bbox": {"l": 327.94131, "t": 119.47247000000004, "r": 345.99634, "b": 124.42969000000005, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Dataset", "bbox": {"l": 488.9942, "t": 104.15374999999983, "r": 508.76384999999993, "b": 109.11095999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "doesn't", "bbox": {"l": 490.1893, "t": 110.27373999999998, "r": 508.76349000000005, "b": 115.2309600000001, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "provide", "bbox": {"l": 489.72009, "t": 116.39373999999998, "r": 508.76758, "b": 121.35095000000013, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "bboxes", "bbox": {"l": 490.71121, "t": 122.51373000000001, "r": 508.76624, "b": 127.47095000000002, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Simple", "bbox": {"l": 185.37759, "t": 141.71118, "r": 202.84102, "b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74854278564453, "r": 544.938232421875, "b": 147.59091186523438, "coord_origin": "TOPLEFT"}, "confidence": 0.6033818125724792, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, 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Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629555344581604, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, 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[{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 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713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126598238945007, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74854278564453, "r": 544.938232421875, "b": 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"b": 146.66840000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "230K 280K", "bbox": {"l": 168.50357, "t": 86.13611000000003, "r": 197.52699, "b": 91.09331999999995, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "65K", "bbox": {"l": 357.3768, "t": 85.99707000000001, "r": 367.39523, "b": 90.95428000000004, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Complex Simple", "bbox": {"l": 333.73151, "t": 141.62323000000004, "r": 374.92862, "b": 146.58043999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "47K", "bbox": {"l": 345.69101, "t": 86.05591000000004, "r": 355.70944, "b": 91.01312000000007, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Simple", "bbox": {"l": 508.54248, "t": 141.37683000000004, "r": 526.00592, "b": 146.33405000000005, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 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"bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "145K", "bbox": {"l": 510.44653000000005, "t": 86.09258999999986, "r": 523.70703, "b": 91.0498, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629555344581604, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126598238945007, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74854278564453, "r": 544.938232421875, "b": 147.59091186523438, "coord_origin": "TOPLEFT"}, "confidence": 0.6033818125724792, "cells": [{"id": 0, "text": "PubTabNet", "bbox": {"l": 119.39108, "t": 77.31055000000003, "r": 151.94641, "b": 83.25922000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "b.", "bbox": {"l": 53.345978, "t": 75.19152999999994, "r": 59.327053, "b": 81.14020000000005, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "FinTabNet", "bbox": {"l": 289.5791, "t": 77.45830999999998, "r": 319.8266, "b": 83.40698000000009, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table Bank", "bbox": {"l": 448.37271, "t": 77.25396999999987, "r": 481.75916, "b": 83.20263999999997, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Train", "bbox": {"l": 82.553436, "t": 141.27617999999995, "r": 94.976013, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Complex", "bbox": {"l": 63.03878399999999, "t": 101.10413000000005, "r": 85.290085, "b": 106.06133999999986, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Simple", "bbox": {"l": 67.76786, "t": 124.39531999999997, "r": 85.231277, "b": 129.35253999999998, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Complex", "bbox": {"l": 227.55121, "t": 102.53992000000005, "r": 249.80251, "b": 107.49712999999997, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Simple", "bbox": {"l": 232.19898999999998, "t": 126.98577999999986, "r": 249.66241, "b": 131.94299, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Simple", "bbox": {"l": 396.2337, "t": 114.04522999999995, "r": 413.69711, "b": 119.00243999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Val", "bbox": {"l": 97.382202, "t": 141.27617999999995, "r": 105.08014, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "100%", "bbox": {"l": 60.93763400000001, "t": 85.73321999999996, "r": 76.151443, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "500K 10K", "bbox": {"l": 82.304901, "t": 86.22351000000003, "r": 106.99162, "b": 91.18073000000015, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Train Test Val", "bbox": {"l": 246.20530999999997, "t": 141.60608000000002, "r": 281.88013, "b": 146.56329000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "100%", "bbox": {"l": 226.69780000000003, "t": 85.73321999999996, "r": 241.91161, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "91K 10K 10K", "bbox": {"l": 249.93848999999997, "t": 86.08801000000005, "r": 282.49384, "b": 91.04522999999995, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Train Test Val", "bbox": {"l": 410.19409, "t": 141.27617999999995, "r": 444.68915, "b": 146.23339999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "100% 130K 5K", "bbox": {"l": 391.37341, "t": 85.73321999999996, "r": 432.6716599999999, "b": 90.69042999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "10K", "bbox": {"l": 435.60571000000004, "t": 86.26140999999996, "r": 445.62414999999993, "b": 91.21862999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Complex", "bbox": {"l": 113.94921, "t": 141.28845, "r": 136.20052, "b": 146.24567000000002, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Non", "bbox": {"l": 116.91554000000001, "t": 94.81853999999998, "r": 127.05433999999998, "b": 99.77575999999999, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Strict", "bbox": {"l": 113.3146, "t": 100.93853999999999, "r": 127.05298, "b": 105.89575000000002, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "HTML", "bbox": {"l": 112.94112, "t": 107.05853000000013, "r": 127.05537, "b": 112.01575000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Strict", "bbox": {"l": 113.22738999999999, "t": 122.61523, "r": 126.96577, "b": 127.57245, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "HTML", "bbox": {"l": 112.85390000000001, "t": 128.73523, "r": 126.96814999999998, "b": 133.69244000000003, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Simple", "bbox": {"l": 138.57864, "t": 141.43640000000005, "r": 156.04207, "b": 146.39362000000006, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "230K 280K", "bbox": {"l": 122.03101, "t": 86.2713, "r": 151.04185, "b": 91.22852, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "65K", "bbox": {"l": 311.65359, "t": 86.55498999999998, "r": 321.67203, "b": 91.5122100000001, "coord_origin": 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"TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"id": 59, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "bbox": {"l": 50.112, "t": 165.50238000000002, "r": 545.11371, "b": 174.40894000000003, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "bbox": {"l": 50.112, "t": 177.4574, "r": 513.52234, "b": 186.36395000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 286.36511, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"id": 61, "text": "\u2022", "bbox": {"l": 61.569, "t": 210.93140000000005, "r": 71.14743, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "TableFormer output does not include the table cell con-", "bbox": {"l": 73.542038, "t": 210.93140000000005, "r": 286.36511, "b": 219.83794999999998, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "tent.", "bbox": {"l": 70.037003, "t": 222.88640999999996, "r": 87.47155, "b": 231.79296999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 286.36514, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9629555344581604, "cells": [{"id": 64, "text": "\u2022", "bbox": {"l": 61.569, "t": 244.07141000000001, "r": 71.345718, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "There are occasional inaccuracies in the predictions of", "bbox": {"l": 73.789902, "t": 244.07141000000001, "r": 286.36514, "b": 252.97797000000003, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "the bounding boxes.", "bbox": {"l": 70.037003, "t": 256.02643, "r": 150.41524, "b": 264.93298000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.20343, "r": 286.36511, "b": 395.70688, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"id": 67, "text": "However, it is possible to mitigate those limitations by", "bbox": {"l": 62.067001, "t": 279.20343, "r": 286.36499, "b": 288.10999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "combining the TableFormer predictions with the informa-", "bbox": {"l": 50.112, "t": 291.15842, "r": 286.36505, "b": 300.06497, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "tion already present inside a programmatic PDF document.", "bbox": {"l": 50.112, "t": 303.1134, "r": 286.36511, "b": 312.01996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "More specifically, PDF documents can be seen as a se-", "bbox": {"l": 50.112, "t": 315.06839, "r": 286.36511, "b": 323.97495, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "quence of PDF cells where each cell is described by its con-", "bbox": {"l": 50.112, "t": 327.02438, "r": 286.36511, "b": 335.93093999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "tent and bounding box. If we are able to associate the PDF", "bbox": {"l": 50.112, "t": 338.97937, "r": 286.36505, "b": 347.88593, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "cells with the predicted table cells, we can directly link the", "bbox": {"l": 50.112, "t": 350.93436, "r": 286.36508, "b": 359.84091, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "PDF cell content to the table cell structure and use the PDF", "bbox": {"l": 50.112, "t": 362.88934, "r": 286.36511, "b": 371.7959, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "bounding boxes to correct misalignments in the predicted", "bbox": {"l": 50.112, "t": 374.84433000000007, "r": 286.36508, "b": 383.75089, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "table cell bounding boxes.", "bbox": {"l": 50.112, "t": 386.80032, "r": 154.55988, "b": 395.70688, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.06934, "r": 286.36496, "b": 419.93188, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"id": 77, "text": "Here is a step-by-step description of the prediction post-", "bbox": {"l": 62.067001, "t": 399.06934, "r": 286.36496, "b": 407.97589, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "processing:", "bbox": {"l": 50.112, "t": 411.02533, "r": 95.491638, "b": 419.93188, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.29532, "r": 286.36508, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"id": 79, "text": "1.", "bbox": {"l": 62.067001, "t": 423.29532, "r": 69.37281, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "Get the minimal grid dimensions - number of rows and", "bbox": {"l": 71.808075, "t": 423.29532, "r": 286.36502, "b": 432.20187, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "columns for the predicted table structure. This represents", "bbox": {"l": 50.112, "t": 435.25031, "r": 286.36508, "b": 444.15686, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "the most granular grid for the underlying table structure.", "bbox": {"l": 50.112, "t": 447.20529, "r": 274.50958, "b": 456.11185000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.47528, "r": 286.36505, "b": 504.2468, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"id": 83, "text": "2.", "bbox": {"l": 62.067001, "t": 459.47528, "r": 69.538948, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Generate pair-wise matches between the bounding", "bbox": {"l": 77.429329, "t": 459.47528, "r": 286.36499, "b": 468.38184, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "bbox": {"l": 50.112, "t": 471.43027, "r": 286.36505, "b": 480.33682, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "bbox": {"l": 50.112, "t": 483.38525, "r": 286.36505, "b": 492.29181, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "of the matches.", "bbox": {"l": 50.112, "t": 495.34024, "r": 110.70452999999999, "b": 504.2468, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.61023, "r": 286.36493, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"id": 88, "text": "3.", "bbox": {"l": 62.067001, "t": 507.61023, "r": 69.863068, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Use a carefully selected IOU threshold to designate", "bbox": {"l": 72.461754, "t": 507.61023, "r": 286.36493, "b": 516.5167799999999, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "the matches as \u201cgood\u201d ones and \u201cbad\u201d ones.", "bbox": {"l": 50.112, "t": 519.5662199999999, "r": 226.0714, "b": 528.4727800000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as \u201cgood\u201d ones and \u201cbad\u201d ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 531.83521, "r": 286.36511, "b": 564.65277, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"id": 91, "text": "3.a. If all IOU scores in a column are below the thresh-", "bbox": {"l": 62.067001, "t": 531.83521, "r": 286.36496, "b": 540.7417800000001, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "old, discard all predictions (structure and bounding boxes)", "bbox": {"l": 50.112, "t": 543.79121, "r": 286.36511, "b": 552.69777, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "for that column.", "bbox": {"l": 50.112, "t": 555.74622, "r": 114.03204, "b": 564.65277, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.01622, "r": 286.36508, "b": 600.83278, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"id": 94, "text": "4.", "bbox": {"l": 62.067001, "t": 568.01622, "r": 69.538948, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Find the best-fitting content alignment for the pre-", "bbox": {"l": 76.731949, "t": 568.01622, "r": 286.36502, "b": 576.92278, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "dicted cells with good IOU per each column. The alignment", "bbox": {"l": 50.112, "t": 579.97122, "r": 286.36508, "b": 588.87778, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "of the column can be identified by the following formula:", "bbox": {"l": 50.112, "t": 591.9262200000001, "r": 278.70383, "b": 600.83278, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.70499, "t": 623.43591, "r": 286.3624, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"id": 98, "text": "alignment", "bbox": {"l": 112.02799999999999, "t": 623.99382, "r": 157.9516, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "= arg min", "bbox": {"l": 160.715, "t": 623.99382, "r": 203.4964, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "c", "bbox": {"l": 185.58499, "t": 633.98305, "r": 189.14511, "b": 640.17578, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "{", "bbox": {"l": 203.49899, "t": 623.43591, "r": 208.48029, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "D$_{c}$", "bbox": {"l": 208.48099, "t": 623.99382, "r": 220.28911, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "}", "bbox": {"l": 220.78699, "t": 623.43591, "r": 225.76828, "b": 632.84061, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "D$_{c}$", "bbox": {"l": 110.70499, "t": 645.25882, "r": 122.51310999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "=", "bbox": {"l": 125.77899000000001, "t": 645.25882, "r": 133.52791, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "max", "bbox": {"l": 136.295, "t": 645.25882, "r": 156.00201, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "{", "bbox": {"l": 156.00299, "t": 644.70091, "r": 160.98428, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "x$_{c}$", "bbox": {"l": 160.98399, "t": 645.25882, "r": 170.23811, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "} \u2212", "bbox": {"l": 170.73599, "t": 644.70091, "r": 185.6779, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "min", "bbox": {"l": 187.894, "t": 645.25882, "r": 206.05283, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "{", "bbox": {"l": 206.054, "t": 644.70091, "r": 211.03529, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "x$_{c}$", "bbox": {"l": 211.035, "t": 645.25882, "r": 220.28912, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "}", "bbox": {"l": 220.787, "t": 644.70091, "r": 225.76829999999998, "b": 654.1056100000001, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "(4)", "bbox": {"l": 274.746, "t": 634.88522, "r": 286.3624, "b": 643.79178, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "alignment = arg min c { D$_{c}$ } D$_{c}$ = max { x$_{c}$ } \u2212 min { x$_{c}$ } (4)"}, {"label": "text", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 667.3479199999999, "r": 286.362, "b": 688.92679, "coord_origin": "TOPLEFT"}, "confidence": 0.9545555114746094, "cells": [{"id": 115, "text": "where", "bbox": {"l": 50.112, "t": 668.06522, "r": 74.45063, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "c", "bbox": {"l": 78.335999, "t": 667.90582, "r": 82.647812, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "is one of", "bbox": {"l": 86.532997, "t": 668.06522, "r": 123.63372, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "{", "bbox": {"l": 127.51899999999999, "t": 667.3479199999999, "r": 132.50029, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "left, centroid, right", "bbox": {"l": 132.50099, "t": 668.06522, "r": 210.69743, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "}", "bbox": {"l": 210.69699, "t": 667.3479199999999, "r": 215.67828, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "and", "bbox": {"l": 219.56299, "t": 668.06522, "r": 233.94897000000003, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "x$_{c}$", "bbox": {"l": 237.83499000000003, "t": 667.90582, "r": 247.08911, "b": 676.75261, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "is the x-", "bbox": {"l": 251.47299000000004, "t": 668.06522, "r": 286.362, "b": 676.97179, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "coordinate for the corresponding point.", "bbox": {"l": 50.112, "t": 680.02022, "r": 205.88721, "b": 688.92679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.290222, "r": 286.36496, "b": 713.151787, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"id": 125, "text": "5.", "bbox": {"l": 62.067001, "t": 692.290222, "r": 69.538948, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Use the alignment computed in step 4, to compute", "bbox": {"l": 76.273666, "t": 692.290222, "r": 286.36496, "b": 701.196785, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "the median", "bbox": {"l": 50.112, "t": 704.245224, "r": 94.604973, "b": 713.151787, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "x", "bbox": {"l": 97.598999, "t": 704.085815, "r": 103.29263, "b": 712.93261, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "-coordinate for all table columns and the me-", "bbox": {"l": 103.292, "t": 704.245224, "r": 286.36481, "b": 713.151787, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 255.7038, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"id": 130, "text": "dian cell size for all table cells. The usage of median dur-", "bbox": {"l": 308.862, "t": 210.93120999999996, "r": 545.11517, "b": 219.83776999999998, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "ing the computations, helps to eliminate outliers caused by", "bbox": {"l": 308.862, "t": 222.88720999999998, "r": 545.11511, "b": 231.79376000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "occasional column spans which are usually wider than the", "bbox": {"l": 308.862, "t": 234.84222, "r": 545.11511, "b": 243.74878, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "normal.", "bbox": {"l": 308.862, "t": 246.79724, "r": 339.57669, "b": 255.7038, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.10222999999996, "r": 545.11499, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"id": 134, "text": "6.", "bbox": {"l": 320.81699, "t": 259.10222999999996, "r": 328.28894, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Snap all cells with bad IOU to their corresponding", "bbox": {"l": 334.88419, "t": 259.10222999999996, "r": 545.11499, "b": 268.00879, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "median", "bbox": {"l": 308.862, "t": 271.05724999999995, "r": 338.19189, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "x", "bbox": {"l": 340.68201, "t": 270.89783, "r": 346.37564, "b": 279.74463000000003, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "-coordinates and cell sizes.", "bbox": {"l": 346.37601, "t": 271.05724999999995, "r": 453.72305000000006, "b": 279.96380999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.86203, "t": 283.36325000000005, "r": 545.11511, "b": 387.91071, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"id": 139, "text": "7.", "bbox": {"l": 320.81702, "t": 283.36325000000005, "r": 328.38953, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "Generate a new set of pair-wise matches between the", "bbox": {"l": 330.9137, "t": 283.36325000000005, "r": 545.11499, "b": 292.26981, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "corrected bounding boxes and PDF cells. This time use a", "bbox": {"l": 308.86203, "t": 295.31824, "r": 545.11511, "b": 304.22479, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "modified version of the IOU metric, where the area of the", "bbox": {"l": 308.86203, "t": 307.27322, "r": 545.11505, "b": 316.17978, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "intersection between the predicted and PDF cells is divided", "bbox": {"l": 308.86203, "t": 319.22821000000005, "r": 545.11511, "b": 328.13477, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "by the PDF cell area.", "bbox": {"l": 308.86203, "t": 331.1842, "r": 397.19043, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "In case there are multiple matches", "bbox": {"l": 403.65616, "t": 331.1842, "r": 545.11511, "b": 340.09076000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "for the same PDF cell, the prediction with the higher score", "bbox": {"l": 308.86203, "t": 343.13919, "r": 545.11511, "b": 352.04575, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "is preferred. This covers the cases where the PDF cells are", "bbox": {"l": 308.86203, "t": 355.09418, "r": 545.11505, "b": 364.00073, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "smaller than the area of predicted or corrected prediction", "bbox": {"l": 308.86203, "t": 367.04916, "r": 545.11505, "b": 375.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "cells.", "bbox": {"l": 308.86203, "t": 379.00415, "r": 329.61414, "b": 387.91071, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.86203, "t": 391.31015, "r": 545.11517, "b": 459.99164, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"id": 150, "text": "8.", "bbox": {"l": 320.81702, "t": 391.31015, "r": 328.55356, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "In some rare occasions, we have noticed that Table-", "bbox": {"l": 331.13242, "t": 391.31015, "r": 545.11505, "b": 400.2167099999999, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "Former can confuse a single column as two. When the post-", "bbox": {"l": 308.86203, "t": 403.26514, "r": 545.11517, "b": 412.17169, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "processing steps are applied, this results with two predicted", "bbox": {"l": 308.86203, "t": 415.22012000000007, "r": 545.11511, "b": 424.12668, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "columns pointing to the same PDF column. In such case", "bbox": {"l": 308.86203, "t": 427.17511, "r": 545.11511, "b": 436.0816699999999, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "we must de-duplicate the columns according to highest to-", "bbox": {"l": 308.86203, "t": 439.1301, "r": 545.11505, "b": 448.03665, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "tal column intersection score.", "bbox": {"l": 308.86203, "t": 451.08507999999995, "r": 426.18161, "b": 459.99164, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.86203, "t": 463.39108, "r": 545.11517, "b": 567.93858, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"id": 157, "text": "9.", "bbox": {"l": 320.81702, "t": 463.39108, "r": 328.67316, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "Pick up the remaining orphan cells. There could be", "bbox": {"l": 331.29187, "t": 463.39108, "r": 545.11499, "b": 472.29764, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "cases, when after applying all the previous post-processing", "bbox": {"l": 308.86203, "t": 475.34607, "r": 545.11505, "b": 484.25262, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "steps, some PDF cells could still remain without any match", "bbox": {"l": 308.86203, "t": 487.30106, "r": 545.11517, "b": 496.20761, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "to predicted cells.", "bbox": {"l": 308.86203, "t": 499.25604, "r": 381.89786, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "However, it is still possible to deduce", "bbox": {"l": 388.7023, "t": 499.25604, "r": 545.11517, "b": 508.1626, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "the correct matching for an orphan PDF cell by mapping its", "bbox": {"l": 308.86203, "t": 511.21204, "r": 545.11511, "b": 520.11859, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "bounding box on the geometry of the grid. This mapping", "bbox": {"l": 308.86203, "t": 523.16702, "r": 545.11505, "b": 532.07358, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "decides if the content of the orphan cell will be appended to", "bbox": {"l": 308.86203, "t": 535.12201, "r": 545.11499, "b": 544.02858, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "an already matched table cell, or a new table cell should be", "bbox": {"l": 308.86203, "t": 547.07703, "r": 545.11517, "b": 555.98358, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "created to match with the orphan.", "bbox": {"l": 308.86203, "t": 559.03203, "r": 442.22147000000007, "b": 567.93858, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.86203, "t": 571.33803, "r": 545.11688, "b": 604.15459, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"id": 168, "text": "9a. Compute the top and bottom boundary of the hori-", "bbox": {"l": 320.81702, "t": 571.33803, "r": 545.11493, "b": 580.24458, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "zontal band for each grid row (min/max", "bbox": {"l": 308.86203, "t": 583.29303, "r": 471.64093, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "y", "bbox": {"l": 474.83405, "t": 583.1336200000001, "r": 479.71872, "b": 591.98041, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "coordinates per", "bbox": {"l": 483.26903999999996, "t": 583.29303, "r": 545.11688, "b": 592.19958, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "row).", "bbox": {"l": 308.86206, "t": 595.24803, "r": 329.91306, "b": 604.15459, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.86206, "t": 607.55304, "r": 545.11505, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"id": 173, "text": "9b.", "bbox": {"l": 320.81705, "t": 607.55304, "r": 332.8718, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "Intersect the orphan\u2019s bounding box with the row", "bbox": {"l": 339.92532, "t": 607.55304, "r": 545.11505, "b": 616.4595899999999, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "bands, and map the cell to the closest grid row.", "bbox": {"l": 308.86206, "t": 619.50903, "r": 495.2923, "b": 628.4155900000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9b. Intersect the orphan\u2019s bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.86206, "t": 631.81403, "r": 545.11505, "b": 664.63059, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"id": 176, "text": "9c. Compute the left and right boundary of the vertical", "bbox": {"l": 320.81705, "t": 631.81403, "r": 545.11505, "b": 640.72058, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "band for each grid column (min/max", "bbox": {"l": 308.86206, "t": 643.7690299999999, "r": 455.28238, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "x", "bbox": {"l": 457.77704, "t": 643.60962, "r": 463.47067, "b": 652.45641, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "coordinates per col-", "bbox": {"l": 465.97104, "t": 643.7690299999999, "r": 545.11389, "b": 652.67558, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "umn).", "bbox": {"l": 308.86206, "t": 655.72403, "r": 332.38376, "b": 664.63059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.86206, "t": 668.03003, "r": 545.11499, "b": 688.89159, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"id": 181, "text": "9d. Intersect the orphan\u2019s bounding box with the column", "bbox": {"l": 320.81705, "t": 668.03003, "r": 545.11499, "b": 676.93659, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "bands, and map the cell to the closest grid column.", "bbox": {"l": 308.86206, "t": 679.98503, "r": 510.5848700000001, "b": 688.89159, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9d. Intersect the orphan\u2019s bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.86206, "t": 692.290024, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"id": 183, "text": "9e. If the table cell under the identified row and column", "bbox": {"l": 320.81705, "t": 692.290024, "r": 545.11505, "b": 701.196594, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "is not empty, extend its content with the content of the or-", "bbox": {"l": 308.86206, "t": 704.245026, "r": 545.11517, "b": 713.151596, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}], "headers": [{"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}, "confidence": 0.9126598238945007, "cells": [{"id": 185, "text": "12", "bbox": {"l": 292.63107, "t": 734.13303, "r": 302.59366, "b": 743.039593, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "phan cell.", "bbox": {"l": 50.112, "t": 75.20836999999995, "r": 88.846588, "b": 84.11492999999996, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9f. 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index 8ad507ed..44506402 100644
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Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 100.21663665771484, "r": 157.03125, "b": 93.70310974121094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 92.24663543701172, "r": 166.94093322753906, "b": 85.73310852050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "https://doi.org/10.1145/3534678.3539043", "text": "https://doi.org/10.1145/3534678.3539043"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 251.91700744628906, "r": 559.8057861328125, "b": 232.48475646972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "Figure 1: Four examples of complex page layouts across different document categories", "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.86951, "t": 440.21915, "r": 330.41248, "b": 438.04535, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 460.42731000000003, "r": 351.16092, "b": 458.68829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 458.81708, "r": 348.30536, "b": 457.07806, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "MODELS AY11230/11234", "text": "MODELS AY11230/11234"}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 455.59561, "r": 329.05914, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.67368, "t": 455.59561, "r": 349.95349, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "The vertical tube can be used for", "text": "The vertical tube can be used for"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11752, "t": 454.16412, "r": 353.57977, "b": 452.64248999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "instructional viewing or to photograph", "text": "instructional viewing or to photograph"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.77121, "t": 452.73264, "r": 352.4306, "b": 451.211, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "the image with a digital camera or a", "text": "the image with a digital camera or a"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.15176, "t": 451.30118, "r": 337.91086, "b": 449.77951, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "micro TV unit", "text": "micro TV unit"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.8313, "t": 449.80956999999995, "r": 329.09155, "b": 448.28793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/26", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.72168, "t": 449.80956999999995, "r": 354.9267, "b": 448.28793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Loosen the retention screw, then rotate", "text": "Loosen the retention screw, then rotate"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.8313, "t": 448.37808, "r": 351.66949, "b": 446.85645, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "the adjustment ring to change the", "text": "the adjustment ring to change the"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.21185, "t": 446.94662, "r": 346.33179, "b": 445.42496, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "length of the vertical tube.", "text": "length of the vertical tube."}, {"self_ref": "#/texts/29", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 445.15319999999997, "r": 329.12726, "b": 443.63153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/30", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.77588, "t": 445.15319999999997, "r": 351.18005, "b": 443.63153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Make sure that both the images in", "text": "Make sure that both the images in"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.25311, "t": 537.05188, "r": 350.07861, "b": 533.13904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "OPERATION", "text": "OPERATION"}, {"self_ref": "#/texts/32", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.07861, "t": 537.23218, "r": 351.82651, "b": 533.31934, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "(", "text": "("}, {"self_ref": "#/texts/33", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 351.82651, "t": 537.05188, "r": 360.85242, "b": 533.13904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "cont.", "text": "cont."}, {"self_ref": "#/texts/34", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.85242, "t": 537.23218, "r": 362.60028, "b": 533.31934, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ")", "text": ")"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 528.50507, "r": 345.84351, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "SELECTING OBJECTIVE", "text": "SELECTING OBJECTIVE"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 526.89484, "r": 340.54153, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MAGNIFICATION", "text": "MAGNIFICATION"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 517.23358, "r": 354.59546, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 505.96207, "r": 354.6499, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupillary distance.", "text": "pupils is the interpupillary distance."}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 504.35187, "r": 328.25125, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.93671, "t": 504.35187, "r": 354.29825, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "To adjust the interpupillary distance", "text": "To adjust the interpupillary distance"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 502.74164, "r": 355.02075, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "rotate the prism caps until both eyes", "text": "rotate the prism caps until both eyes"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 501.13144000000005, "r": 350.82028, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 499.52121, "r": 336.2067, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 496.30078, "r": 335.3941, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.07379, "t": 494.69058, "r": 353.18555, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Remove the lens protective cover.", "text": "Remove the lens protective cover."}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 493.08035, "r": 328.35919, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.0972, "t": 493.08035, "r": 353.45065, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Place the specimen on the working", "text": "Place the specimen on the working"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 491.47015, "r": 333.32825, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 489.85991999999993, "r": 328.31296, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.02783, "t": 489.85991999999993, "r": 354.76303, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Focus the specimen with the left eye", "text": "Focus the specimen with the left eye"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 488.24973, "r": 355.96307, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 483.41907, "r": 355.38867, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 478.58844, "r": 344.13388, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 476.97821000000005, "r": 328.37418, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11963, "t": 476.97821000000005, "r": 348.50162, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Disconnect the power cord.", "text": "Disconnect the power cord."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 475.36801, "r": 328.34061, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06931, "t": 475.36801, "r": 353.11588, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 472.14757999999995, "r": 348.02094, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 470.53735, "r": 328.37512, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.12036, "t": 470.53735, "r": 352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 468.92715, "r": 328.36884, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.1102, "t": 468.92715, "r": 356.5412, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Open the window in the base plate and", "text": "Open the window in the base plate and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 528.50507, "r": 366.93256, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 526.89484, "r": 359.89841, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.63751, "t": 526.89484, "r": 387.98407, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Turn the focusing knob away or toward", "text": "Turn the focusing knob away or toward"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 525.28467, "r": 384.58948, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/98", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 504.35187, "r": 367.98694, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 502.74164, "r": 359.80386, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.49353, "t": 502.74164, "r": 386.70093, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "If the image is not clear to both eyes", "text": "If the image is not clear to both eyes"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 501.13144000000005, "r": 388.03534, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 499.52121, "r": 373.13724, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment."}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 494.69058, "r": 359.83682, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.54297, "t": 494.69058, "r": 388.08289, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "To adjust the eyepiece for viewing with", "text": "To adjust the eyepiece for viewing with"}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 493.08035, "r": 382.73251, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 491.47015, "r": 387.72266, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 489.85991999999993, "r": 384.1991, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "and left eyes, follow the following", "text": "and left eyes, follow the following"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right eyepiece"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 462.4863, "r": 382.63382, "b": 460.74725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 459.26587000000006, "r": 375.67661, "b": 457.52682000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 457.65564, "r": 359.90311, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 456.04544, "r": 372.01416, "b": 454.3064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet."}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 454.43521, "r": 359.88327, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.61191, "t": 454.43521, "r": 384.65726, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 452.82501, "r": 385.33649, "b": 451.0859699999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 451.21478, "r": 379.57224, "b": 449.47577, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4274, "t": 449.60458, "r": 359.91788, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.66312, "t": 449.60458, "r": 384.5108, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 447.99434999999994, "r": 359.92792, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.67746, "t": 447.99434999999994, "r": 385.41235, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Open the window in the base plate", "text": "Open the window in the base plate"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 446.38416, "r": 383.2782, "b": 444.64511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "and replace the halogen lamp or", "text": "and replace the halogen lamp or"}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 444.77393, "r": 383.13953, "b": 443.03491, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 443.16373, "r": 368.43472, "b": 441.42468, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/148", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.59567, "t": 530.85815, "r": 339.11377, "b": 529.11908, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Model AY11230", "text": "Model AY11230"}, {"self_ref": "#/texts/149", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.48605, "t": 530.85815, "r": 371.00415, "b": 529.11908, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Model AY11234", "text": "Model AY11234"}, {"self_ref": "#/texts/150", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 455.43533, "t": 440.22961000000004, "r": 457.97827000000007, "b": 438.05585, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.24518, "t": 516.47327, "r": 414.4234, "b": 515.03979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Objectives", "text": "Objectives"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.39554, "t": 523.01764, "r": 419.06677, "b": 521.58417, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Revolving Turret", "text": "Revolving Turret"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 512.87372, "r": 445.87192, "b": 511.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Coarse", "text": "Coarse"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 511.69391, "r": 448.22338999999994, "b": 510.2604099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 510.51407, "r": 444.40371999999996, "b": 509.08060000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.79288, "t": 537.05353, "r": 428.91568, "b": 533.14069, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MODEL AY11236", "text": "MODEL AY11236"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.32535, "t": 486.95709, "r": 435.93542, "b": 483.04427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "MICROSCOPE USAGE", "text": "MICROSCOPE USAGE"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 481.64108, "r": 453.72171, "b": 479.46729, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "BARSKA Model AY11236 is a powerful fixed power compound", "text": "BARSKA Model AY11236 is a powerful fixed power compound"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 479.49414, "r": 453.09939999999995, "b": 477.32034, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "microscope designed for biological studies such as specimen", "text": "microscope designed for biological studies such as specimen"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 477.3472, "r": 456.65246999999994, "b": 475.1734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "examination. It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 475.20023, "r": 456.73859000000004, "b": 473.02646, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses."}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.62399, "t": 471.57059, "r": 427.77472, "b": 467.65777999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CONSTRUCTION", "text": "CONSTRUCTION"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 465.53930999999994, "r": 456.02639999999997, "b": 463.36551, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope."}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 463.3923300000001, "r": 455.42238999999995, "b": 461.2185400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 461.24539, "r": 457.39844, "b": 459.07159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted illumination. By using this instrument,", "text": "equipped with transmitted illumination. By using this instrument,"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 459.09845, "r": 453.97745, "b": 456.92464999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "the user can observe specimens at magnification from 40x to", "text": "the user can observe specimens at magnification from 40x to"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 456.95148, "r": 454.70708999999994, "b": 454.77768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "1000x by selecting the desired objective lens. Coarse and fine", "text": "1000x by selecting the desired objective lens. Coarse and fine"}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 454.80453, "r": 458.90240000000006, "b": 452.63074, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "focus adjustments provide accuracy and image detail. The rotating", "text": "focus adjustments provide accuracy and image detail. The rotating"}, {"self_ref": "#/texts/169", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 452.65759, "r": 453.0672, "b": 450.4838, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "head allows the user to position the eyepieces for maximum", "text": "head allows the user to position the eyepieces for maximum"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 450.51062, "r": 449.63113, "b": 448.33682, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "viewing comfort and easy access to all adjustment knobs.", "text": "viewing comfort and easy access to all adjustment knobs."}, {"self_ref": "#/texts/171", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 422.10626, "t": 490.75809, "r": 434.62433000000004, "b": 489.01904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Model AY11236", "text": "Model AY11236"}, {"self_ref": "#/texts/172", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 508.91351, "r": 444.8817399999999, "b": 507.48004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Fine", "text": "Fine"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 507.7337, "r": 448.85001, "b": 506.30019999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 442.01610999999997, "t": 506.55389, "r": 445.03033000000005, "b": 505.12039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.00577, "t": 512.87421, "r": 411.42212, "b": 511.4407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stage", "text": "Stage"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 511.0855700000001, "r": 410.77707, "b": 509.6521, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Condenser", "text": "Condenser"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 509.90576, "r": 409.2157, "b": 508.47226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Focusing", "text": "Focusing"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 508.72592, "r": 407.08594, "b": 507.2924499999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.81281, "t": 529.67822, "r": 447.03702, "b": 528.24475, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Eyepiece", "text": "Eyepiece"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 437.34607, "t": 520.86975, "r": 440.80496, "b": 519.43719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stand", "text": "Stand"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 507.59973, "r": 413.3768, "b": 506.16718, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 506.16837, "r": 413.68201, "b": 504.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "On/Off", "text": "On/Off"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 504.737, "r": 413.6337, "b": 503.30447, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Switch", "text": "Switch"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.8712499999999, "t": 495.2847, "r": 438.53164999999996, "b": 493.85217, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 499.81692999999996, "r": 443.08768, "b": 498.38439999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Power", "text": "Power"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 498.38556, "r": 442.29575, "b": 496.95303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Cord", "text": "Cord"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 413.55829, "t": 527.33911, "r": 421.94913, "b": 525.90656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Rotating Head", "text": "Rotating Head"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 505.09427, "r": 447.87585000000007, "b": 503.66174, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Stage Clip", "text": "Stage Clip"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 503.6629, "r": 448.67252, "b": 502.23037999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 407.2403, "t": 532.13354, "r": 425.79089, "b": 530.70105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Interpupillary Slide Adjustment", "text": "Interpupillary Slide Adjustment"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 413.33698, "r": 466.08835000000005, "b": 411.21588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Circling Minimums", "text": "Circling Minimums"}, {"self_ref": "#/texts/192", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 408.7796000000001, "r": 449.64444, "b": 406.65851000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/193", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.18811, "t": 408.7796000000001, "r": 550.77124, "b": 406.65851000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 184]}], "orig": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H", "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 406.24268, "r": 536.14716, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a", "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 538.31085, "t": 406.24268, "r": 549.49921, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "is placed on", "text": "is placed on"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 403.96399, "r": 547.58185, "b": 401.8429, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 532.05829, "t": 398.7871999999999, "r": 550.42261, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "on the circling line of", "text": "on the circling line of"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 396.50851, "r": 455.74692, "b": 394.38745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "minima.", "text": "minima."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 376.40451, "r": 496.2829, "b": 374.49554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", 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"27"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.7934, "t": 396.9834, "r": 342.1416, "b": 394.72687, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "February 2013", "text": "February 2013"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 342.64841, "t": 396.81702, "r": 342.65811, "b": 395.50142999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47177, "t": 396.98526, "r": 386.25897, "b": 394.7287, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "2013 interim result and interim dividend announced", "text": "2013 interim result and interim dividend announced"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40491, "t": 394.44250000000005, "r": 331.02695, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.83795, "t": 394.44250000000005, "r": 340.75909, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2013", "text": "August 2013"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 341.26437, "t": 394.2746, "r": 341.27408, "b": 392.95905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47144, "t": 394.44287, "r": 385.93265, "b": 392.18631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "2013 full year results and fi nal dividend announced", "text": "2013 full year results and fi nal dividend announced"}, {"self_ref": "#/texts/259", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 391.53845, "r": 329.87708, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/260", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 330.34882, "t": 391.53845, "r": 358.65204, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Indicative dates only, subject to change/Board confi rmation", "text": "Indicative dates only, subject to change/Board confi rmation"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 387.65497, "r": 391.771, "b": 385.39844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney", "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 385.62143, "r": 369.65308, "b": 383.36486999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012."}, {"self_ref": "#/texts/263", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 331.46945000000005, "r": 379.25955, "b": 326.45493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Ye s te rd ay", "text": "Ye s te rd ay"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/265", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/266", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/267", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/268", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/269", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "In 1841, when AGL supplied the gas to light", "text": "In 1841, when AGL supplied the gas to light"}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 313.08301, "r": 393.75891, "b": 310.82648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "the fi rst public street lamp, it was reported", "text": "the fi rst public street lamp, it was reported"}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 311.04947, "r": 390.4975, "b": 308.79291, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "in the Sydney Gazette as a \u201cwonderful", "text": "in the Sydney Gazette as a \u201cwonderful"}, {"self_ref": "#/texts/272", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 309.0159, "r": 395.70975, "b": 306.75937, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "achievement of scientifi c knowledge, assisted", "text": "achievement of scientifi c knowledge, assisted"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 306.98236, "r": 394.27283, "b": 304.7258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "by mechanical ingenuity.\u201d Within two years,", "text": "by mechanical ingenuity.\u201d Within two years,"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 304.94879, "r": 396.65939, "b": 302.69226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "165 gas lamps were lighting the City of Sydney.", "text": "165 gas lamps were lighting the City of Sydney."}, {"self_ref": "#/texts/275", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 372.06876, "r": 384.19696, "b": 360.90588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Looking back on", "text": "Looking back on"}, {"self_ref": "#/texts/276", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 361.89621, "r": 372.16626, "b": 350.73331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "175 years of", "text": "175 years of"}, {"self_ref": "#/texts/277", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.4054, "t": 351.72363000000007, "r": 385.3981, "b": 340.56076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "looking forward.", "text": "looking forward."}, {"self_ref": "#/texts/278", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 419.83841, "r": 353.36179, "b": 418.08331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "AGL Energy Limited ABN 74 115 061 375", "text": "AGL Energy Limited ABN 74 115 061 375"}, {"self_ref": "#/texts/279", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 546.20587, "t": 431.09552, "r": 548.23407, "b": 429.17758, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "29", "text": "29"}, {"self_ref": "#/texts/280", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 497.77728, "t": 540.56616, "r": 542.8255, "b": 537.05615, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "signs, signals and road markings", "text": "signs, signals and road markings"}, {"self_ref": "#/texts/281", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 490.30679, "t": 540.52521, "r": 492.09982, "b": 537.0152, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/282", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 528.11078, "r": 500.05637, "b": 526.07281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "In", "text": "In"}, {"self_ref": "#/texts/283", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 500.05637, "t": 528.14282, "r": 524.37036, "b": 526.1369, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "chapter 2, you and your vehicle", "text": "chapter 2, you and your vehicle"}, {"self_ref": "#/texts/284", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 524.37036, "t": 528.11078, "r": 539.89124, "b": 526.07281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": ", you learned about", "text": ", you learned about"}, {"self_ref": "#/texts/285", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 526.06775, "r": 544.50403, "b": 524.02979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "some of the controls in your vehicle. This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. Understanding these three ways", "text": "the messages printed on them. Understanding these three ways"}, {"self_ref": "#/texts/292", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 506.09204, "r": 545.26471, "b": 504.05408, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "of classifying signs will help you figure out the meaning of signs", "text": "of classifying signs will help you figure out the meaning of signs"}, {"self_ref": "#/texts/293", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 504.04901, "r": 513.31335, "b": 502.01105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "that are new to you.", "text": "that are new to you."}, {"self_ref": "#/texts/294", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 505.43439, "t": 488.92404, "r": 508.53033000000005, "b": 487.10361, "coord_origin": "BOTTOMLEFT"}, "charspan": 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[], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.87561, "t": 470.24866, "r": 543.32263, "b": 468.42822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "yellow-green", "text": "yellow-green"}, {"self_ref": "#/texts/302", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 499.21862999999996, "t": 453.87228, "r": 512.62451, "b": 452.05185, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Tells about motorist", "text": "Tells about motorist"}, {"self_ref": "#/texts/303", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 499.21862999999996, "t": 452.18468999999993, "r": 504.39917, "b": 450.36426, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "services", "text": "services"}, {"self_ref": "#/texts/304", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 516.97748, "t": 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"children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 534.5141, "t": 437.3826, "r": 540.2818, "b": 435.56216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "direction", "text": "direction"}, {"self_ref": "#/texts/315", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.37466, "t": 521.85925, "r": 479.14251999999993, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/316", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.91036999999994, "t": 521.85925, "r": 483.74963, "b": 519.82123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/317", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 519.15283, "r": 492.31219, "b": 517.65112, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "- regulatory signs", "text": "- regulatory signs"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 516.85486, "r": 486.72598000000005, "b": 515.35321, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "- school,", "text": "- school,"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 515.22028, "r": 492.93286000000006, "b": 513.18231, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "playground and", "text": "playground and"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 513.17725, "r": 491.82938000000007, "b": 511.13925, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "crosswalk signs", "text": "crosswalk signs"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 510.47241, "r": 491.00775000000004, "b": 508.97076, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- lane use signs", "text": "- lane use signs"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 508.17444, "r": 493.32748, "b": 506.6727900000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "- turn control signs", "text": "- turn control signs"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 505.8765, "r": 490.4915199999999, "b": 504.37482, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- parking signs", "text": "- parking signs"}, {"self_ref": "#/texts/324", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 503.57852, "r": 491.17004000000003, "b": 502.07684, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/325", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 501.94394000000005, "r": 484.77405000000005, "b": 499.90594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "signs", "text": "signs"}, {"self_ref": "#/texts/326", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 499.23830999999996, "r": 490.83398, "b": 497.73666, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- warning signs", "text": "- warning signs"}, {"self_ref": "#/texts/327", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 496.94037, "r": 491.62692, "b": 495.43869, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- object markers", "text": "- object markers"}, {"self_ref": "#/texts/328", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 494.6424, "r": 490.37341, "b": 493.1407500000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- construction", "text": "- construction"}, {"self_ref": "#/texts/329", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 493.00781, "r": 484.77405000000005, "b": 490.96985, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "signs", "text": "signs"}, {"self_ref": "#/texts/330", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 490.30219000000005, "r": 492.93912, "b": 488.80054, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "- information and", "text": "- information and"}, {"self_ref": "#/texts/331", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 488.6676, "r": 493.00525, "b": 486.62964, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "destination signs", "text": "destination signs"}, {"self_ref": "#/texts/332", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 485.9620100000001, "r": 489.99047999999993, "b": 484.46033, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- railway signs", "text": "- railway signs"}, {"self_ref": "#/texts/333", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.375, "t": 483.75211, "r": 479.1032400000001, "b": 481.71414, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/334", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.83151, "t": 483.75211, "r": 484.92925999999994, "b": 481.71414, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Signals", "text": "Signals"}, {"self_ref": "#/texts/335", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 481.04642, "r": 490.00091999999995, "b": 479.54474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- lane control", "text": "- lane control"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 479.4118000000001, "r": 485.95331, "b": 477.37384, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "signals", "text": "signals"}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 476.70621, "r": 489.29876999999993, "b": 475.20456, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- traffic lights", "text": "- traffic lights"}, {"self_ref": "#/texts/338", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.375, "t": 474.49634, "r": 479.18129999999996, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022"}, {"self_ref": "#/texts/339", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.98761, "t": 474.49634, "r": 490.46960000000007, "b": 472.4583400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Road markings", "text": "Road markings"}, {"self_ref": "#/texts/340", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 471.79062, "r": 489.26166000000006, "b": 470.28897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- yellow lines", "text": "- yellow lines"}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 469.49268, "r": 488.59189, "b": 467.991, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "- white lines", "text": "- white lines"}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 467.1947, "r": 491.17004000000003, "b": 465.69302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 481.21602999999993, "t": 465.56012, "r": 487.58978, "b": 463.52216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "markings", "text": "markings"}, {"self_ref": "#/texts/344", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 462.85449, "r": 491.75177, "b": 461.35284, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "- other markings", "text": "- other markings"}, {"self_ref": "#/texts/345", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.15246999999994, "t": 526.92969, "r": 493.75586, "b": 523.93127, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "in this chapter", "text": "in this chapter"}, {"self_ref": "#/texts/346", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 199.53408813476562, "r": 379.82049560546875, "b": 189.22499084472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "level": 1}, {"self_ref": "#/texts/347", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 184.3324432373047, "r": 559.1859741210938, "b": 164.9988250732422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.65997314453125, "t": 151.94566345214844, "r": 404.6536560058594, "b": 144.41390991210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ACM Reference Format:", "text": "ACM Reference Format:", "level": 1}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 141.88003540039062, "r": 559.5494995117188, "b": 84.62297058105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/356", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/357", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/373", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/386", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/387", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/430", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 349.16577, "t": 545.31982, "r": 352.48175, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 385.93698, "t": 545.31982, "r": 392.56894, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 424.366, "t": 545.31982, "r": 430.99796, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "40", "text": 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538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 573.61536, "r": 337.56735, "b": 567.54126, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 599.91339, "r": 337.56735, "b": 593.83929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 626.21136, "r": 337.56735, "b": 620.13727, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 652.5094, "r": 337.56735, "b": 646.4353, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 678.80737, "r": 337.56735, "b": 672.73328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 322.92276, "t": 643.62311, "r": 328.99686, "b": 605.20782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 0.50:0.95", "text": "mAP 0.50:0.95"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 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"parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 575.99994, "r": 411.54321, "b": 569.92584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/455", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 591.77875, "r": 411.54321, "b": 585.70465, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/456", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 607.55756, "r": 411.54321, "b": 601.48346, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 623.33636, "r": 411.54321, "b": 617.26227, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/458", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 639.11511, "r": 411.54321, "b": 633.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/459", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/461", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/487", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Rueschlikon, Switzerland dol@zurich.ibm.com"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 172.54302978515625, "t": 599.942626953125, "r": 275.3072509765625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.6930236816406, "t": 599.942626953125, "r": 439.457275390625, "b": 553.3746948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79803466796875, "t": 544.297119140625, "r": 111.94354248046875, "b": 533.9879760742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.46699905395508, "t": 529.095458984375, "r": 295.5601806640625, "b": 257.7068176269531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 241.00308227539062, "r": 134.81988525390625, "b": 230.69398498535156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79798889160156, "t": 225.91700744628906, "r": 297.8529357910156, "b": 195.4988555908203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;", "text": "\u00b7 Information systems \u2192 Document structure ; \u00b7 Applied computing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 157.60162353515625, "r": 295.11798095703125, "b": 119.2081069946289, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 116.91976928710938, "r": 197.8627471923828, "b": 110.43414306640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.31700134277344, "t": 108.18763732910156, "r": 186.74652099609375, "b": 101.67411041259766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.55400085449219, "t": 100.21663665771484, "r": 157.03125, "b": 93.70310974121094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.79800033569336, "t": 92.24663543701172, "r": 166.94093322753906, "b": 85.73310852050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "https://doi.org/10.1145/3534678.3539043", "text": "https://doi.org/10.1145/3534678.3539043"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 251.91700744628906, "r": 559.8057861328125, "b": 232.48475646972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "Figure 1: Four examples of complex page layouts across different document categories", "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.86951, "t": 440.21915, "r": 330.41248, "b": 438.04535, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 460.42731000000003, "r": 351.16092, "b": 458.68829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 458.81708, "r": 348.30536, "b": 457.07806, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "MODELS AY11230/11234", "text": "MODELS AY11230/11234"}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 455.59561, "r": 329.05914, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.67368, "t": 455.59561, "r": 349.95349, "b": 454.07394, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "The vertical tube can be used for", "text": "The vertical tube can be used for"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11752, "t": 454.16412, "r": 353.57977, "b": 452.64248999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "instructional viewing or to photograph", "text": "instructional viewing or to photograph"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.77121, "t": 452.73264, "r": 352.4306, "b": 451.211, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "the image with a digital camera or a", "text": "the image with a digital camera or a"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.15176, "t": 451.30118, "r": 337.91086, "b": 449.77951, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "micro TV unit", "text": "micro TV unit"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.8313, "t": 449.80956999999995, "r": 329.09155, "b": 448.28793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/26", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.72168, "t": 449.80956999999995, "r": 354.9267, "b": 448.28793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Loosen the retention screw, then rotate", "text": "Loosen the retention screw, then rotate"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.8313, "t": 448.37808, "r": 351.66949, "b": 446.85645, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "the adjustment ring to change the", "text": "the adjustment ring to change the"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.21185, "t": 446.94662, "r": 346.33179, "b": 445.42496, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "length of the vertical tube.", "text": "length of the vertical tube."}, {"self_ref": "#/texts/29", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83005, "t": 445.15319999999997, "r": 329.12726, "b": 443.63153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/30", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.77588, "t": 445.15319999999997, "r": 351.18005, "b": 443.63153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Make sure that both the images in", "text": "Make sure that both the images in"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.25311, "t": 537.05188, "r": 350.07861, "b": 533.13904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "OPERATION", "text": "OPERATION"}, {"self_ref": "#/texts/32", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.07861, "t": 537.23218, "r": 351.82651, "b": 533.31934, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "(", "text": "("}, {"self_ref": "#/texts/33", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 351.82651, "t": 537.05188, "r": 360.85242, "b": 533.13904, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "cont.", "text": "cont."}, {"self_ref": "#/texts/34", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.85242, "t": 537.23218, "r": 362.60028, "b": 533.31934, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ")", "text": ")"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 528.50507, "r": 345.84351, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "SELECTING OBJECTIVE", "text": "SELECTING OBJECTIVE"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 526.89484, "r": 340.54153, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MAGNIFICATION", "text": "MAGNIFICATION"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 525.28467, "r": 328.31903, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.03836, "t": 525.28467, "r": 354.21472, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "There are two objectives. The lower", "text": "There are two objectives. The lower"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 523.67444, "r": 355.19193, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 522.06421, "r": 345.80057, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 520.45398, "r": 328.33862, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06775, "t": 520.45398, "r": 352.39969, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "In order to observe the specimen", "text": "In order to observe the specimen"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 518.84381, "r": 352.90042, "b": 517.10474, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 517.23358, "r": 354.59546, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 515.62335, "r": 350.81885, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 514.01312, "r": 335.46707, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed."}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 510.79272, "r": 354.57755, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 509.18249999999995, "r": 335.1752, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 507.5723, "r": 328.34784, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.08157, "t": 507.5723, "r": 354.76245, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The distance between the observer's", "text": "The distance between the observer's"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 505.96207, "r": 354.6499, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupillary distance.", "text": "pupils is the interpupillary distance."}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88037, "t": 504.35187, "r": 328.25125, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.93671, "t": 504.35187, "r": 354.29825, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "To adjust the interpupillary distance", "text": "To adjust the interpupillary distance"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 502.74164, "r": 355.02075, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "rotate the prism caps until both eyes", "text": "rotate the prism caps until both eyes"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 501.13144000000005, "r": 350.82028, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 499.52121, "r": 336.2067, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 496.30078, "r": 335.3941, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88181, "t": 494.69058, "r": 328.34314, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.07379, "t": 494.69058, "r": 353.18555, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Remove the lens protective cover.", "text": "Remove the lens protective cover."}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 493.08035, "r": 328.35919, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.0972, "t": 493.08035, "r": 353.45065, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Place the specimen on the working", "text": "Place the specimen on the working"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 491.47015, "r": 333.32825, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 489.85991999999993, "r": 328.31296, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.02783, "t": 489.85991999999993, "r": 354.76303, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Focus the specimen with the left eye", "text": "Focus the specimen with the left eye"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 488.24973, "r": 355.96307, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 486.6395, "r": 354.46594, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 485.0293, "r": 328.25488, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 328.9407, "t": 485.0293, "r": 356.37335, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Rotate the right eyepiece ring until the", "text": "Rotate the right eyepiece ring until the"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 483.41907, "r": 355.38867, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 481.80887, "r": 343.17249, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear."}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 478.58844, "r": 344.13388, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 476.97821000000005, "r": 328.37418, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.11963, "t": 476.97821000000005, "r": 348.50162, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Disconnect the power cord.", "text": "Disconnect the power cord."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88324, "t": 475.36801, "r": 328.34061, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.06931, "t": 475.36801, "r": 353.11588, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 473.7577800000001, "r": 353.79517, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 472.14757999999995, "r": 348.02094, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88464, "t": 470.53735, "r": 328.37512, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.12036, "t": 470.53735, "r": 352.96808, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 468.92715, "r": 328.36884, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.1102, "t": 468.92715, "r": 356.5412, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Open the window in the base plate and", "text": "Open the window in the base plate and"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 467.31692999999996, "r": 350.13828, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 465.70673, "r": 351.59677, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88608, "t": 464.0965, "r": 336.89197, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "illuminator.", "text": "illuminator."}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 528.50507, "r": 366.93256, "b": 526.76605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 526.89484, "r": 359.89841, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.63751, "t": 526.89484, "r": 387.98407, "b": 525.15582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Turn the focusing knob away or toward", "text": "Turn the focusing knob away or toward"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42023, "t": 525.28467, "r": 384.58948, "b": 523.54559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed."}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42166, "t": 523.67444, "r": 359.78549, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.46741, "t": 523.67444, "r": 384.33441, "b": 521.93542, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "If the image is unclear, adjust the", "text": "If the image is unclear, adjust the"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 522.06421, "r": 384.61502, "b": 520.3252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 520.45398, "r": 385.38922, "b": 518.71497, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again."}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 517.23358, "r": 377.35046, "b": 515.49457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 515.62335, "r": 359.89429, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.62988, "t": 515.62335, "r": 386.37589, "b": 513.88434, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Turn the zoom magnification knob to", "text": "Turn the zoom magnification knob to"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 514.01312, "r": 386.78732, "b": 512.27411, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 512.40295, "r": 364.16855, "b": 510.66391, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view."}, {"self_ref": "#/texts/98", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 510.79272, "r": 359.86777, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/99", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.59012, "t": 510.79272, "r": 387.31656, "b": 509.05368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "In most situations, it is recommended", "text": "In most situations, it is recommended"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 509.18249999999995, "r": 381.56656, "b": 507.44348, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4231, "t": 507.5723, "r": 386.63403, "b": 505.83325, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 505.96207, "r": 382.77115, "b": 504.22305, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 504.35187, "r": 367.98694, "b": 502.61282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary."}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 502.74164, "r": 359.80386, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/105", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.49353, "t": 502.74164, "r": 386.70093, "b": 501.00262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "If the image is not clear to both eyes", "text": "If the image is not clear to both eyes"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 501.13144000000005, "r": 388.03534, "b": 499.3924, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 499.52121, "r": 373.13724, "b": 497.7822, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment."}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 496.30078, "r": 381.74539, "b": 494.56177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT"}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 494.69058, "r": 359.83682, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.54297, "t": 494.69058, "r": 388.08289, "b": 492.95154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "To adjust the eyepiece for viewing with", "text": "To adjust the eyepiece for viewing with"}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 493.08035, "r": 382.73251, "b": 491.34134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 491.47015, "r": 387.72266, "b": 489.73110999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right"}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 489.85991999999993, "r": 384.1991, "b": 488.1209099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "and left eyes, follow the following", "text": "and left eyes, follow the following"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 488.24973, "r": 364.88672, "b": 486.51068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 486.6395, "r": 359.95078, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "a.", "text": "a."}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.47699, "t": 486.6395, "r": 386.65988, "b": 484.90047999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Observe an image through the left", "text": "Observe an image through the left"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 485.0293, "r": 386.7634, "b": 483.29025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 483.41907, "r": 385.41354, "b": 481.68005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob."}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42453, "t": 481.80887, "r": 359.93304, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b."}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 361.44156, "t": 481.80887, "r": 382.56085, "b": 480.06982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "By turning the diopter ring", "text": "By turning the diopter ring"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 480.19864, "r": 385.4559, "b": 478.45963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "adjustment for the left eyepiece,", "text": "adjustment for the left eyepiece,"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 478.58844, "r": 384.56122, "b": 476.84940000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "bring the same point into sharp", "text": "bring the same point into sharp"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 476.97821000000005, "r": 366.74371, "b": 475.23920000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "focus.", "text": "focus."}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 475.36801, "r": 383.93884, "b": 473.62897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "c.Then bring the same point into", "text": "c.Then bring the same point into"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 473.7577800000001, "r": 385.69241, "b": 472.0187700000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "focus through the right eyepiece", "text": "focus through the right eyepiece"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 472.14757999999995, "r": 385.94861, "b": 470.40854, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 470.53735, "r": 385.54236, "b": 468.79834, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 468.92715, "r": 382.98718, "b": 467.18811, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewer should note their own", "text": "viewer should note their own"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 467.31692999999996, "r": 385.06448, "b": 465.57791, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "diopter ring position for the left", "text": "diopter ring position for the left"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 465.70673, "r": 385.20682, "b": 463.96768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "and right eyepieces, then before", "text": "and right eyepieces, then before"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 464.0965, "r": 382.21964, "b": 462.35748, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring"}, {"self_ref": 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"label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.64169, "t": 457.65564, "r": 385.75333, "b": 455.91663, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "Disconnect the power cord from the", "text": "Disconnect the power cord from the"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 456.04544, "r": 372.01416, "b": 454.3064, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet."}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 454.43521, "r": 359.88327, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2."}, {"self_ref": "#/texts/138", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.61191, "t": 454.43521, "r": 384.65726, "b": 452.6962, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "When the bulb is cool, remove the", "text": "When the bulb is cool, remove the"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 452.82501, "r": 385.33649, "b": 451.0859699999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique illuminator cap and remove", "text": "oblique illuminator cap and remove"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42596, "t": 451.21478, "r": 379.57224, "b": 449.47577, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.4274, "t": 449.60458, "r": 359.91788, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.66312, "t": 449.60458, "r": 384.5108, "b": 447.86553999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Replace with a new halogen bulb.", "text": "Replace with a new halogen bulb."}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 447.99434999999994, "r": 359.92792, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4."}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 360.67746, "t": 447.99434999999994, "r": 385.41235, "b": 446.25534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Open the window in the base plate", "text": "Open the window in the base plate"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 446.38416, "r": 383.2782, "b": 444.64511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "and replace the halogen lamp or", "text": "and replace the halogen lamp or"}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 444.77393, "r": 383.13953, "b": 443.03491, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted"}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42883, "t": 443.16373, "r": 368.43472, "b": 441.42468, 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{"self_ref": "#/texts/151", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.24518, "t": 516.47327, "r": 414.4234, "b": 515.03979, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Objectives", "text": "Objectives"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.39554, "t": 523.01764, "r": 419.06677, "b": 521.58417, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Revolving Turret", "text": "Revolving Turret"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 512.87372, "r": 445.87192, "b": 511.44025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Coarse", "text": "Coarse"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 511.69391, "r": 448.22338999999994, "b": 510.2604099999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.3895, "t": 510.51407, "r": 444.40371999999996, "b": 509.08060000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.79288, "t": 537.05353, "r": 428.91568, "b": 533.14069, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "MODEL AY11236", "text": "MODEL AY11236"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.32535, "t": 486.95709, "r": 435.93542, "b": 483.04427999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "MICROSCOPE USAGE", "text": "MICROSCOPE USAGE"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 481.64108, "r": 453.72171, "b": 479.46729, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "BARSKA Model AY11236 is a powerful fixed power compound", "text": "BARSKA Model AY11236 is a powerful fixed power compound"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 479.49414, "r": 453.09939999999995, "b": 477.32034, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "microscope designed for biological studies such as specimen", "text": "microscope designed for biological studies such as specimen"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 477.3472, "r": 456.65246999999994, "b": 475.1734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "examination. It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 475.20023, "r": 456.73859000000004, "b": 473.02646, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses."}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.62399, "t": 471.57059, "r": 427.77472, "b": 467.65777999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CONSTRUCTION", "text": "CONSTRUCTION"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 465.53930999999994, "r": 456.02639999999997, "b": 463.36551, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope."}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 463.3923300000001, "r": 455.42238999999995, "b": 461.2185400000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 461.24539, "r": 457.39844, "b": 459.07159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted illumination. By using this instrument,", "text": "equipped with transmitted illumination. By using this instrument,"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 459.09845, "r": 453.97745, "b": 456.92464999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "the user can observe specimens at magnification from 40x to", "text": "the user can observe specimens at magnification from 40x to"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 456.95148, "r": 454.70708999999994, "b": 454.77768, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "1000x by selecting the desired objective lens. Coarse and fine", "text": "1000x by selecting the desired objective lens. Coarse and fine"}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08414, "t": 454.80453, "r": 458.90240000000006, "b": 452.63074, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "focus adjustments provide accuracy and image detail. The rotating", "text": "focus adjustments provide accuracy and image detail. The rotating"}, {"self_ref": "#/texts/169", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 452.65759, "r": 453.0672, "b": 450.4838, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "head allows the user to position the eyepieces for maximum", "text": "head allows the user to position the eyepieces for maximum"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.08594, "t": 450.51062, "r": 449.63113, "b": 448.33682, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "viewing comfort and easy access to all adjustment knobs.", "text": "viewing comfort and easy access to all adjustment knobs."}, {"self_ref": "#/texts/171", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 422.10626, "t": 490.75809, "r": 434.62433000000004, "b": 489.01904, 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"Knob"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 408.00577, "t": 512.87421, "r": 411.42212, "b": 511.4407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stage", "text": "Stage"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 511.0855700000001, "r": 410.77707, "b": 509.6521, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Condenser", "text": "Condenser"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 509.90576, "r": 409.2157, "b": 508.47226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Focusing", "text": "Focusing"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 404.07172, "t": 508.72592, "r": 407.08594, "b": 507.2924499999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.81281, "t": 529.67822, "r": 447.03702, "b": 528.24475, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Eyepiece", "text": "Eyepiece"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 437.34607, "t": 520.86975, "r": 440.80496, "b": 519.43719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Stand", "text": "Stand"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 507.59973, "r": 413.3768, "b": 506.16718, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 506.16837, "r": 413.68201, "b": 504.73584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "On/Off", "text": "On/Off"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 409.7164, "t": 504.737, "r": 413.6337, "b": 503.30447, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Switch", "text": "Switch"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.8712499999999, "t": 495.2847, "r": 438.53164999999996, "b": 493.85217, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Lamp", "text": "Lamp"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 499.81692999999996, "r": 443.08768, "b": 498.38439999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Power", "text": "Power"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 439.52039, "t": 498.38556, "r": 442.29575, "b": 496.95303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Cord", "text": "Cord"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 413.55829, "t": 527.33911, "r": 421.94913, "b": 525.90656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Rotating Head", "text": "Rotating Head"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 505.09427, "r": 447.87585000000007, "b": 503.66174, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Stage Clip", "text": "Stage Clip"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.84316999999993, "t": 503.6629, "r": 448.67252, "b": 502.23037999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 407.2403, "t": 532.13354, "r": 425.79089, "b": 530.70105, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Interpupillary Slide Adjustment", "text": "Interpupillary Slide Adjustment"}, {"self_ref": "#/texts/191", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 413.33698, "r": 466.08835000000005, "b": 411.21588, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Circling Minimums", "text": "Circling Minimums"}, {"self_ref": "#/texts/192", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 408.7796000000001, "r": 449.64444, "b": 406.65851000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/193", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.18811, "t": 408.7796000000001, "r": 550.77124, "b": 406.65851000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 184]}], "orig": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H", "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 406.24268, "r": 536.14716, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a", "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 538.31085, "t": 406.24268, "r": 549.49921, "b": 404.12158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "is placed on", "text": "is placed on"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 403.96399, "r": 547.58185, "b": 401.8429, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 119]}], "orig": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP."}, {"self_ref": "#/texts/197", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 398.7871999999999, "r": 449.6163, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.1319, "t": 398.7871999999999, "r": 529.53082, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H", "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 532.05829, "t": 398.7871999999999, "r": 550.42261, "b": 396.66614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "on the circling line of", "text": "on the circling line of"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 396.50851, "r": 455.74692, "b": 394.38745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "minima.", "text": "minima."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 376.40451, "r": 496.2829, "b": 374.49554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H", "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H"}, {"self_ref": "#/texts/202", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 382.74457, "r": 551.16101, "b": 380.8356, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V", "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 501.13077, "t": 380.69376, "r": 505.2477999999999, "b": 378.78479, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 371.81198, "r": 469.35599, "b": 369.26669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "AIRPORT SKETCH", "text": "AIRPORT SKETCH"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 366.91092, "r": 525.93616, "b": 364.78983, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related", "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 364.6322, "r": 522.0343, "b": 362.51114, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 362.35352, "r": 524.67151, "b": 360.23245, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "lot recognition of the airport from the air and to provide some information to aid on ground", "text": "lot recognition of the airport from the air and to provide some information to aid on ground"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 360.07485999999994, "r": 527.172, "b": 357.95377, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway", "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 357.79617, "r": 502.39545, "b": 355.67508, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "dimensions (length and width) are shown for all active runways.", "text": "dimensions (length and width) are shown for all active runways."}, {"self_ref": "#/texts/210", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 353.2388000000001, "r": 512.92676, "b": 351.11771000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text": "Runway(s) are depicted based on what type and construction of the runway."}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 347.92999, "r": 460.02307, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 347.92999, "r": 473.98819, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Other Than", "text": "Other Than"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 345.87915, "r": 474.96744, "b": 343.97021, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Hard Surface", "text": "Hard Surface"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 347.92999, "r": 489.45648, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Metal Surface", "text": "Metal Surface"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 347.92999, "r": 505.03076, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Closed Runway", "text": "Closed Runway"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 509.5809, "t": 347.92999, "r": 524.30237, "b": 346.02099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Under Construction", "text": "Under Construction"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 337.18793, "r": 458.31406, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Stopways,", "text": "Stopways,"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 335.13712, "r": 461.92083999999994, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Taxiways, Park-", "text": "Taxiways, Park-"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.95525999999995, "t": 333.08627, "r": 457.08014, "b": 331.17731000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "ing Areas", "text": "ing Areas"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 337.18793, "r": 472.87732, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Displaced", "text": "Displaced"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 464.89963, "t": 335.13712, "r": 472.49792, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Threshold", "text": "Threshold"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 337.18793, "r": 483.61584, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Closed", "text": "Closed"}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.91357, "t": 335.13712, "r": 486.60754000000003, "b": 333.22814999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Pavement", "text": "Pavement"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 493.06420999999995, "t": 337.18793, "r": 504.20648, "b": 335.27896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Water Runway", "text": "Water Runway"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 322.67026, "r": 548.59674, "b": 320.54919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 320.39157, "r": 500.08181999999994, "b": 318.27051, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text": "sions, runway slope, arresting gear, and displaced threshold."}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 315.83423, "r": 449.59933000000007, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 450.09796, "t": 315.83423, "r": 547.82562, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L", "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 547.82623, "t": 315.83423, "r": 548.45862, "b": 313.71313, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 313.55554, "r": 470.52609000000007, "b": 311.43445, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "pads may also be shown.", "text": "pads may also be shown."}, {"self_ref": "#/texts/231", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 308.99817, "r": 493.37906000000004, "b": 306.87708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q", "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 449.10074000000003, "t": 304.4408, "r": 551.80023, "b": 295.48364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 496]}], "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone elevation (TDZE) is shown in the upper right corner of the sketch box. 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"BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "AGL 2013 Financial Calendar", "text": "AGL 2013 Financial Calendar"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 409.69727, "r": 330.96848, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "22", "text": "22"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75003, "t": 409.69727, "r": 341.12875, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 409.69727, "r": 384.81079, "b": 407.44073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "2012 full year result and fi nal dividend announced", "text": "2012 full year result and fi nal dividend announced"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 407.15448, "r": 330.97336, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "30", "text": "30"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.75735, "t": 407.15448, "r": 341.16534, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2012", "text": "August 2012"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 407.15448, "r": 372.90613, "b": 404.89795, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Ex-dividend trading commences", "text": "Ex-dividend trading commences"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 404.61172, "r": 330.20337, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.00137, "t": 404.61172, "r": 342.9715, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 404.61172, "r": 374.88693, "b": 402.35516000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Record date for 2012 fi nal dividend", "text": "Record date for 2012 fi nal dividend"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 402.06897, "r": 331.0173, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.82327, "t": 402.06897, "r": 343.91284, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "September 2012", "text": "September 2012"}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.4722, "t": 402.06897, "r": 365.65988, "b": 399.81238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Final dividend payable", "text": "Final dividend payable"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 399.52618, "r": 330.98804, "b": 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"27"}, {"self_ref": "#/texts/252", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.7934, "t": 396.9834, "r": 342.1416, "b": 394.72687, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "February 2013", "text": "February 2013"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 342.64841, "t": 396.81702, "r": 342.65811, "b": 395.50142999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47177, "t": 396.98526, "r": 386.25897, "b": 394.7287, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "2013 interim result and interim dividend announced", "text": "2013 interim result and interim dividend announced"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40491, "t": 394.44250000000005, "r": 331.02695, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.83795, "t": 394.44250000000005, "r": 340.75909, "b": 392.18594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "August 2013", "text": "August 2013"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 341.26437, "t": 394.2746, "r": 341.27408, "b": 392.95905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 350.47144, "t": 394.44287, "r": 385.93265, "b": 392.18631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "2013 full year results and fi nal dividend announced", "text": "2013 full year results and fi nal dividend announced"}, {"self_ref": "#/texts/259", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 391.53845, "r": 329.87708, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/260", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 330.34882, "t": 391.53845, "r": 358.65204, "b": 390.03412, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Indicative dates only, subject to change/Board confi rmation", "text": "Indicative dates only, subject to change/Board confi rmation"}, {"self_ref": "#/texts/261", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 387.65497, "r": 391.771, "b": 385.39844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney", "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney"}, {"self_ref": "#/texts/262", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.40536, "t": 385.62143, "r": 369.65308, "b": 383.36486999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012."}, {"self_ref": "#/texts/263", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 331.46945000000005, "r": 379.25955, "b": 326.45493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Ye s te rd ay", "text": "Ye s te rd ay"}, {"self_ref": "#/texts/264", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 325.2843, "r": 391.38229, "b": 323.02777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Established in Sydney in 1837, and then", "text": "Established in Sydney in 1837, and then"}, {"self_ref": "#/texts/265", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 323.25076, "r": 395.01788, "b": 320.99423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "known as The Australian Gas Light Company,", "text": "known as The Australian Gas Light Company,"}, {"self_ref": "#/texts/266", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 321.21719, "r": 394.08322, "b": 318.96066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "the AGL business has an established history", "text": "the AGL business has an established history"}, {"self_ref": "#/texts/267", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 319.18365, "r": 390.60727, "b": 316.92712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "and reputation for serving the gas and", "text": "and reputation for serving the gas and"}, {"self_ref": "#/texts/268", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 317.15012, "r": 393.49612, "b": 314.89355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "electricity needs of Australian households.", "text": "electricity needs of Australian households."}, {"self_ref": "#/texts/269", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 315.11655, "r": 394.11481, "b": 312.86002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "In 1841, when AGL supplied the gas to light", "text": "In 1841, when AGL supplied the gas to light"}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 313.08301, "r": 393.75891, "b": 310.82648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "the fi rst public street lamp, it was reported", "text": "the fi rst public street lamp, it was reported"}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 311.04947, "r": 390.4975, "b": 308.79291, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "in the Sydney Gazette as a \u201cwonderful", "text": "in the Sydney Gazette as a \u201cwonderful"}, {"self_ref": "#/texts/272", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 309.0159, "r": 395.70975, "b": 306.75937, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "achievement of scientifi c knowledge, assisted", "text": "achievement of scientifi c knowledge, assisted"}, {"self_ref": "#/texts/273", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 306.98236, "r": 394.27283, "b": 304.7258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "by mechanical ingenuity.\u201d Within two years,", "text": "by mechanical ingenuity.\u201d Within two years,"}, {"self_ref": "#/texts/274", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 363.54486, "t": 304.94879, "r": 396.65939, "b": 302.69226, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "165 gas lamps were lighting the City of Sydney.", "text": "165 gas lamps were lighting the City of Sydney."}, 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This chapter is a handy"}, {"self_ref": "#/texts/286", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 524.02466, "r": 544.01343, "b": 521.98669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common"}, {"self_ref": "#/texts/287", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 521.98169, "r": 544.11987, "b": 519.94366, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "signs, signals and road markings that keep traffi c organized", "text": "signs, signals and road markings that keep traffi c organized"}, {"self_ref": "#/texts/288", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 519.9386, "r": 515.41071, "b": 517.90063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly."}, {"self_ref": "#/texts/289", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 514.65381, "r": 505.64642000000003, "b": 511.0643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs"}, {"self_ref": "#/texts/290", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 510.17813, "r": 543.92957, "b": 508.14017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and"}, {"self_ref": "#/texts/291", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.15335, "t": 508.1351, "r": 545.67834, "b": 506.09711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. 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"children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 471.79062, "r": 489.26166000000006, "b": 470.28897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "- yellow lines", "text": "- yellow lines"}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 469.49268, "r": 488.59189, "b": 467.991, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "- white lines", "text": "- white lines"}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.97293, "t": 467.1947, "r": 491.17004000000003, "b": 465.69302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "- reserved lane", "text": "- reserved lane"}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 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189.22499084472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "level": 1}, {"self_ref": "#/texts/347", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 184.3324432373047, "r": 559.1859741210938, "b": 164.9988250732422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning"}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.65997314453125, "t": 151.94566345214844, "r": 404.6536560058594, "b": 144.41390991210938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ACM Reference Format:", "text": "ACM Reference Format:", "level": 1}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.9549865722656, "t": 141.88003540039062, "r": 559.5494995117188, "b": 84.62297058105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.79800033569336, "t": 706.14013671875, "r": 156.52899169921875, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 681.0164794921875, "r": 303.0169677734375, "b": 563.0528564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"self_ref": "#/texts/353", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.52899932861328, "t": 560.4684448242188, "r": 295.5641174316406, "b": 289.0808410644531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1580]}], "orig": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"self_ref": "#/texts/354", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.59199905395508, "t": 286.4964599609375, "r": 295.56396484375, "b": 212.36782836914062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"self_ref": "#/texts/355", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 207.41844177246094, "r": 295.5616455078125, "b": 177.12582397460938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/356", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 174.54144287109375, "r": 294.2625427246094, "b": 155.20883178710938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/357", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 152.62445068359375, "r": 294.6838073730469, "b": 122.33183288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/358", "parent": {"cref": "#/groups/0"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.70800018310547, "t": 119.7474365234375, "r": 295.56439208984375, "b": 100.41383361816406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/359", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672000885009766, "t": 89.77363586425781, "r": 216.02749633789062, "b": 83.2601089477539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"self_ref": "#/texts/360", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.0950012207031, "t": 704.636474609375, "r": 558.4320068359375, "b": 685.3028564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"self_ref": "#/texts/361", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.8650207519531, "t": 682.718505859375, "r": 559.7210083007812, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.62298583984375, "t": 624.0244750976562, "r": 559.1903076171875, "b": 571.8138427734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.7309875488281, "t": 569.2294311523438, "r": 559.5819702148438, "b": 484.142822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"self_ref": "#/texts/364", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 470.7911071777344, "r": 421.7441101074219, "b": 460.4820251464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "level": 1}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.5249938964844, "t": 445.6674499511719, "r": 559.7161254882812, "b": 327.7038269042969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 325.1194763183594, "r": 559.1864624023438, "b": 240.03182983398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 226.6800994873047, "r": 477.4568786621094, "b": 216.37100219726562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "level": 1}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 201.5564422607422, "r": 559.7131958007812, "b": 116.46983337402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.9549865722656, "t": 113.88543701171875, "r": 558.2041015625, "b": 83.59282684326172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 555.885009765625, "r": 294.0437316894531, "b": 536.4527587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"self_ref": "#/texts/373", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.11293, "t": 658.91284, "r": 262.97623, "b": 650.3858, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.87892, "t": 651.53821, "r": 213.89999, "b": 643.01117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%"}, {"self_ref": "#/texts/375", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.13306, "t": 698.8423499999999, "r": 237.64882999999998, "b": 690.31531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific"}, {"self_ref": "#/texts/376", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.40349, "t": 673.31793, "r": 199.66519, "b": 664.79089, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%"}, {"self_ref": "#/texts/377", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288223, "t": 677.6452600000001, "r": 118.80401, "b": 669.1182300000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial"}, {"self_ref": "#/texts/378", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.24422, "t": 661.75592, "r": 151.50592, "b": 653.22888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%"}, {"self_ref": "#/texts/379", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973373, "t": 604.34235, "r": 121.11515, "b": 595.81531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders"}, {"self_ref": "#/texts/380", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.6235, "t": 621.77252, "r": 150.64458, "b": 613.24548, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%"}, {"self_ref": "#/texts/381", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.88339, "t": 579.49963, "r": 157.68491, "b": 570.9726, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws"}, {"self_ref": "#/texts/382", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.43983, "t": 608.22192, "r": 172.70154, "b": 599.69489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%"}, {"self_ref": "#/texts/383", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.47252, "t": 602.70343, "r": 254.29510000000002, "b": 594.17639, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals"}, {"self_ref": "#/texts/384", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.40683, "t": 620.87854, "r": 209.66853, "b": 612.3515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 510.19647216796875, "r": 294.2738342285156, "b": 425.1098327636719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \"text in the wild\"."}, {"self_ref": "#/texts/386", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.57400131225586, "t": 422.52545166015625, "r": 295.5604553222656, "b": 282.6438293457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"self_ref": "#/texts/387", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.46699905395508, "t": 280.0594482421875, "r": 295.5615539550781, "b": 184.01382446289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"self_ref": "#/texts/388", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 181.429443359375, "r": 295.56396484375, "b": 107.30182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.79800033569336, "t": 90.34363555908203, "r": 195.78997802734375, "b": 83.83010864257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.62298583984375, "t": 704.636474609375, "r": 559.1918334960938, "b": 630.5088500976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 627.9244384765625, "r": 558.4381103515625, "b": 520.9197998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.9419860839844, "t": 518.33544921875, "r": 559.7215576171875, "b": 203.11082458496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \"invisible\" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \"invisible\" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \"natural\" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"self_ref": "#/texts/393", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.9549865722656, "t": 185.15008544921875, "r": 470.2132568359375, "b": 174.8409881591797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "level": 1}, {"self_ref": "#/texts/394", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.6860046386719, "t": 160.0264434814453, "r": 559.7138061523438, "b": 85.8978271484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}, {"self_ref": "#/texts/395", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/396", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.50199890136719, "t": 707.0450439453125, "r": 558.4896850585938, "b": 676.65380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \"Total\") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"self_ref": "#/texts/397", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 237.99000549316406, "r": 295.64874267578125, "b": 185.68075561523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"self_ref": "#/texts/398", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.46699905395508, "t": 157.7084503173828, "r": 294.0474548339844, "b": 116.45683288574219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"self_ref": "#/texts/399", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.79800033569336, "t": 113.989013671875, "r": 295.5584411621094, "b": 83.57982635498047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"self_ref": "#/texts/400", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 479.92047119140625, "r": 559.1853637695312, "b": 416.7518310546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"self_ref": "#/texts/401", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 414.1674499511719, "r": 559.7130737304688, "b": 285.2448425292969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"self_ref": "#/texts/402", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.62298583984375, "t": 282.7770080566406, "r": 559.7176513671875, "b": 98.9438247680664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.9549865722656, "t": 89.64663696289062, "r": 369.2456970214844, "b": 83.13311004638672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "$^{3}$https://arxiv.org/", "text": "$^{3}$https://arxiv.org/"}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 704.636474609375, "r": 294.04541015625, "b": 685.2938842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 682.7184448242188, "r": 295.5592346191406, "b": 542.8378295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 540.2534790039062, "r": 295.56005859375, "b": 455.16583251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"self_ref": "#/texts/409", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 443.4874572753906, "r": 294.04620361328125, "b": 402.22686767578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/410", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70799255371094, "t": 399.6514892578125, "r": 295.563720703125, "b": 358.39984130859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/411", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 355.81549072265625, "r": 294.0472412109375, "b": 336.4728698730469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/412", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 333.8984680175781, "r": 294.0459899902344, "b": 314.5648193359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/413", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.70800018310547, "t": 311.98046875, "r": 264.5057067871094, "b": 303.59686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/414", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.7080078125, "t": 301.021484375, "r": 294.0461730957031, "b": 270.72882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/415", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.52899932861328, "t": 259.0494689941406, "r": 295.5625305175781, "b": 217.798828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"self_ref": "#/texts/416", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.79800033569336, "t": 215.3310089111328, "r": 295.562255859375, "b": 86.29182434082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"self_ref": "#/texts/417", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.9549865722656, "t": 318.5060119628906, "r": 559.8057861328125, "b": 288.11480712890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"self_ref": "#/texts/418", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00214, "t": 612.20703, "r": 416.20551, "b": 610.09027, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037"}, {"self_ref": "#/texts/419", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.38269, "t": 706.80933, "r": 417.83722, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines"}, {"self_ref": "#/texts/420", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.42834, "t": 706.80933, "r": 546.22913, "b": 699.716, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative"}, {"self_ref": "#/texts/421", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.33701, "t": 427.14294, "r": 513.48035, "b": 420.04964999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives"}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 546.92615, "r": 416.20538, "b": 544.80939, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860"}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.00201, "t": 432.87512, "r": 416.20538, "b": 430.75833, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4"}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 693.65894, "r": 326.01498, "b": 687.74786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 605.00897, "r": 326.01498, "b": 599.09796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 538.45807, "r": 326.01498, "b": 532.547, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/427", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.19424, "t": 424.91504000000003, "r": 326.01498, "b": 419.004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/428", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.12841796875, "t": 333.5567321777344, "r": 476.331787109375, "b": 331.43994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"self_ref": "#/texts/429", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 266.5024719238281, "r": 558.204345703125, "b": 247.1688232421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"self_ref": "#/texts/430", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.62298583984375, "t": 244.7010040283203, "r": 559.7149047851562, "b": 82.78482818603516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}, {"self_ref": "#/texts/431", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/432", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.64874267578125, "b": 608.98291015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset."}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.52899932861328, "t": 421.07244873046875, "r": 295.5561218261719, "b": 215.43682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.79800033569336, "t": 203.87008666992188, "r": 147.4853515625, "b": 193.5609893798828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "level": 1}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.48400115966797, "t": 178.74644470214844, "r": 295.4281005859375, "b": 82.7008285522461, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 512.9840087890625, "r": 559.8057861328125, "b": 449.7158203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 329]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"self_ref": "#/texts/437", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 349.16577, "t": 545.31982, "r": 352.48175, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0"}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 385.93698, "t": 545.31982, "r": 392.56894, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 424.366, "t": 545.31982, "r": 430.99796, "b": 539.24573, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "40", "text": 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538.19159, "r": 483.47278000000006, "b": 532.11749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "% of DocLayNet training set", "text": "% of DocLayNet training set"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 573.61536, "r": 337.56735, "b": 567.54126, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 599.91339, "r": 337.56735, "b": 593.83929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 626.21136, "r": 337.56735, "b": 620.13727, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 652.5094, "r": 337.56735, "b": 646.4353, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.93539, "t": 678.80737, "r": 337.56735, "b": 672.73328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 322.92276, "t": 643.62311, "r": 328.99686, "b": 605.20782, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 0.50:0.95", "text": "mAP 0.50:0.95"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 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"parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 575.99994, "r": 411.54321, "b": 569.92584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "50", "text": "50"}, {"self_ref": "#/texts/455", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 591.77875, "r": 411.54321, "b": 585.70465, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/456", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 607.55756, "r": 411.54321, "b": 601.48346, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 623.33636, "r": 411.54321, "b": 617.26227, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65"}, {"self_ref": "#/texts/458", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.91125, "t": 639.11511, "r": 411.54321, "b": 633.04102, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70"}, {"self_ref": "#/texts/459", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 407.98846435546875, "r": 558.2041625976562, "b": 388.6548156738281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.6409912109375, "t": 386.0704650878906, "r": 558.4364013671875, "b": 311.9428405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"self_ref": "#/texts/461", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.9549865722656, "t": 295.1781005859375, "r": 466.8532409667969, "b": 284.8690185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/462", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.7489929199219, "t": 279.9754638671875, "r": 558.4308471679688, "b": 85.2998275756836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}, {"self_ref": "#/texts/463", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 347.0172424316406, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/464", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.75701904296875, "t": 731.6909790039062, "r": 558.2028198242188, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/465", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.6486511230469, "b": 663.77685546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"self_ref": "#/texts/466", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 317.65899658203125, "t": 705.1270141601562, "r": 559.8068237304688, "b": 663.7767944335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 189]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 472.4300842285156, "r": 131.05624389648438, "b": 462.1210021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/468", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.78499984741211, "t": 457.22845458984375, "r": 295.558349609375, "b": 262.55181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is \"large enough\". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"self_ref": "#/texts/469", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.79800033569336, "t": 249.49008178710938, "r": 164.3289794921875, "b": 239.1809844970703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/470", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.46699905395508, "t": 234.2884521484375, "r": 295.5567932128906, "b": 83.44783020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"self_ref": "#/texts/471", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 460.5964660644531, "r": 559.5849609375, "b": 375.50982666015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549560546875, "t": 362.6051025390625, "r": 549.860595703125, "b": 352.2960205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.62298583984375, "t": 347.4034729003906, "r": 559.7138061523438, "b": 196.5628204345703, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 852]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.9549865722656, "t": 183.6580810546875, "r": 418.5477600097656, "b": 173.34898376464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.6860046386719, "t": 168.45645141601562, "r": 559.1881713867188, "b": 83.35986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 731.6909790039062, "r": 558.202880859375, "b": 723.4239501953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"self_ref": "#/texts/477", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.50199890136719, "t": 705.1270751953125, "r": 295.648681640625, "b": 641.85888671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"self_ref": "#/texts/478", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 401.0794677734375, "r": 294.047119140625, "b": 348.85986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.46699905395508, "t": 346.28546142578125, "r": 295.55908203125, "b": 206.40382385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.79800033569336, "t": 186.9390869140625, "r": 156.00534057617188, "b": 176.62998962402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.52899932861328, "t": 171.7364501953125, "r": 295.5584411621094, "b": 86.64982604980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"self_ref": "#/texts/482", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.95501708984375, "t": 706.14013671875, "r": 405.7296142578125, "b": 695.8309936523438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "level": 1}, {"self_ref": "#/texts/483", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 690.9384765625, "r": 559.7137451171875, "b": 605.850830078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.6860046386719, "t": 603.2664794921875, "r": 559.717041015625, "b": 507.2208251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"self_ref": "#/texts/485", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.62298583984375, "t": 504.636474609375, "r": 558.4346923828125, "b": 474.3438415527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.9549865722656, "t": 456.9081115722656, "r": 387.3695983886719, "b": 446.5990295410156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "level": 1}, {"self_ref": "#/texts/487", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.197998046875, "t": 443.29766845703125, "r": 558.2009887695312, "b": 420.8371276855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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"row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "-", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 256.4979248046875, "t": 473.3204650878906, "r": 264.836669921875, "b": 464.9458312988281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "84", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 78.53099822998047, "t": 484.2794494628906, "r": 144.6671600341797, "b": 475.9048156738281, "coord_origin": "BOTTOMLEFT"}, "row_span": 5, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DocLayNet (DLN)", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 154.62899780273438, "t": 462.3614501953125, 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.md b/tests/data/groundtruth/docling_v2/2206.01062.md
index c5452c57..4f3872cc 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.md
+++ b/tests/data/groundtruth/docling_v2/2206.01062.md
@@ -55,7 +55,7 @@ In this paper, we present the DocLayNet dataset. It provides pageby-page layout
 
 This enables experimentation with annotation uncertainty and quality control analysis.
 
-- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.
+- (5) Pre-defined Train-, Test- &amp; Validation-set : Like DocBank, we provide fixed train-, test- &amp; validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.
 
 All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.
 
@@ -77,9 +77,9 @@ Figure 2: Distribution of DocLayNet pages across document categories.
 
 <!-- image -->
 
-to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild".
+to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( &gt; 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild".
 
-The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.
+The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws &amp; Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.
 
 We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.
 
@@ -192,7 +192,7 @@ In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], F
 
 Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.
 
-Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.
+Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH&lt;tildelow&gt; 10% point improvement.
 
 | Class-count    |   11 | 6       | 5       | 4       |
 |----------------|------|---------|---------|---------|
@@ -243,7 +243,7 @@ Many documents in DocLayNet have a unique styling. In order to avoid overfitting
 
 Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,
 
-Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.
+Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank &amp; DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.
 
 |                 |            | Testing on   | Testing on   | Testing on   |
 |-----------------|------------|--------------|--------------|--------------|
diff --git a/tests/data/groundtruth/docling_v2/2206.01062.pages.json b/tests/data/groundtruth/docling_v2/2206.01062.pages.json
index 95ae5fbc..5991c6e9 100644
--- a/tests/data/groundtruth/docling_v2/2206.01062.pages.json
+++ b/tests/data/groundtruth/docling_v2/2206.01062.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Ahmed S. Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}, {"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}, {"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}, {"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}, {"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}, {"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}, {"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "section_header", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}, "confidence": 0.8416305184364319, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 90.96701, "t": 133.67236000000003, "r": 193.73123, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8637173771858215, "cells": [{"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 255.11602999999997, "t": 133.67236000000003, "r": 357.88025, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.861267626285553, "cells": [{"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 419.26505, "t": 133.67236000000003, "r": 522.0293, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8760631680488586, "cells": [{"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.54303, "t": 192.05737, "r": 275.30725, "b": 238.62531, "coord_origin": "TOPLEFT"}, "confidence": 0.8319941759109497, "cells": [{"id": 14, "text": "Ahmed S. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107323408126831, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 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327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107323408126831, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095849633216858, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.4083557128906, "t": 250.34860229492188, "r": 553.295166015625, "b": 525.8507690429688, "coord_origin": "TOPLEFT"}, "confidence": 0.5910328030586243, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149601459503174, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458831310272217, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107323408126831, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.5095849633216858, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.4083557128906, "t": 250.34860229492188, "r": 553.295166015625, "b": 525.8507690429688, "coord_origin": "TOPLEFT"}, "confidence": 0.5910328030586243, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208481311798096, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, 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{"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455161690711975, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8537878394126892, "cells": [{"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367702603340149, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802553653717041, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9792875051498413, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795212149620056, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687230587005615, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594183564186096, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615144729614258, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.944757342338562, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8586848378181458, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7996684312820435, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563279151916504, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813893437385559, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.984420895576477, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347407817840576, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.986040472984314, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.986108124256134, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9330969452857971, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9853019118309021, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749841094017029, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7680341601371765, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104616641998291, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.32994079589844, "t": 92.88652801513672, "r": 263.7051696777344, "b": 220.56790161132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9698247909545898, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 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189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425569772720337, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.32994079589844, "t": 92.88652801513672, "r": 263.7051696777344, "b": 220.56790161132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9698247909545898, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, 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"coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 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281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866551160812378, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.9877007007598877, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877579212188721, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880730509757996, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048323035240173, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869208335876465, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.986062228679657, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853487610816956, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422595500946045, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863835573196411, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8920254111289978, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104616641998291, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", "bbox": {"l": 428.1217, "t": 162.53954999999996, "r": 447.82986, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "69-78", "bbox": {"l": 457.80051, "t": 162.53954999999996, "r": 477.50867, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "n/a", "bbox": {"l": 495.32489, "t": 162.53954999999996, "r": 507.17846999999995, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Footnote", "bbox": {"l": 104.825, "t": 173.49854000000005, "r": 137.3282, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "6318", "bbox": {"l": 182.035, "t": 173.49854000000005, "r": 198.71251, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "0.60", "bbox": {"l": 219.211, "t": 173.49854000000005, "r": 233.69174000000004, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "0.31", "bbox": {"l": 250.01956, "t": 173.49854000000005, "r": 264.50031, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "0.58", "bbox": 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{"id": 42, "text": "82-97", "bbox": {"l": 487.47025, "t": 173.49854000000005, "r": 507.17841, "b": 181.87316999999996, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Formula", "bbox": {"l": 104.825, "t": 184.45752000000005, "r": 135.33766, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "25027", "bbox": {"l": 177.866, "t": 184.45752000000005, "r": 198.71288, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.25", "bbox": {"l": 219.211, "t": 184.45752000000005, "r": 233.69174000000004, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1.90", "bbox": {"l": 250.01956, "t": 184.45752000000005, "r": 264.50031, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "2.96", "bbox": {"l": 280.82812, "t": 184.45752000000005, "r": 295.30887, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "83-85", "bbox": {"l": 305.27301, "t": 184.45752000000005, "r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", "bbox": {"l": 104.825, 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Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, 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The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906546831130981, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93096923828125, "t": 137.47544860839844, "r": 512.5798950195312, "b": 294.08154296875, "coord_origin": "TOPLEFT"}, "confidence": 0.9860088229179382, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, 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53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.98039311170578, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748358726501465, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818443655967712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866706728935242, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847550392150879, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080382585525513, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.059146881103516, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.8641357421875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847874045372009, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625212669372559, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 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"Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 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"children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022756218910217, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.97113436460495, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.959128201007843, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.941342830657959, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.942673921585083, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147459268569946, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544310569763184, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.9601745605469, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264114022254944, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873723745346069, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611716270446777, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877841830253601, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534681797027588, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.866277813911438, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 62.02744674682617, "t": 195.68003845214844, "r": 285.78948974609375, "b": 351.6619567871094, "coord_origin": "TOPLEFT"}, "confidence": 0.9883638620376587, "cells": [{"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, 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Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782734513282776, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419493079185486, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.484130859375, "t": 89.88618469238281, "r": 553.5410766601562, "b": 260.00982666015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9782701730728149, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.963992178440094, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594222903251648, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872913956642151, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.95790034532547, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877227544784546, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.866277813911438, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316120743751526, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318176507949829, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.829693615436554, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.3552017211914, "t": 150.93629455566406, "r": 267.0082702636719, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, 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"coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 352.9774475097656, "t": 150.79129028320312, "r": 522.9158935546875, "b": 306.2658996582031, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.829693615436554, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 80.3552017211914, "t": 150.93629455566406, "r": 267.0082702636719, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736217617988586, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740145802497864, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562515616416931, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872550964355469, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316120743751526, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318176507949829, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.65901947021484, "t": 172.4807891845703, "r": 274.8346862792969, "b": 339.85406494140625, "coord_origin": "TOPLEFT"}, "confidence": 0.9892617464065552, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, 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"bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868071675300598, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859861135482788, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9319987893104553, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9854921698570251, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800511598587036, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.967609703540802, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372861385345459, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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ACM, 2018.", "bbox": {"l": 409.56598, "t": 590.82736, "r": 507.31765999999993, "b": 597.3409, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018."}, {"label": "list_item", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 317.95499, "t": 598.79736, "r": 559.3783, "b": 613.28787, "coord_origin": "TOPLEFT"}, "confidence": 0.8972867131233215, "cells": [{"id": 78, "text": "[23]", "bbox": {"l": 317.95499, "t": 598.79736, "r": 329.37976, "b": 605.3109, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Connor Shorten and Taghi M. Khoshgoftaar. 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Journal of Big Data , 6(1):60, 2019."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021661043167114, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429464101791382, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
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Nassar", "bbox": {"l": 182.26804, "t": 192.05737, "r": 265.39255, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "IBM Research", "bbox": {"l": 195.87103, "t": 205.40923999999995, "r": 251.98038999999997, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Rueschlikon, Switzerland", "bbox": {"l": 172.54303, "t": 217.36425999999994, "r": 275.30725, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "ahn@zurich.ibm.com", "bbox": {"l": 180.52803, "t": 229.32025, "r": 267.3222, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Peter Staar", "bbox": {"l": 361.52802, "t": 192.05737, "r": 414.84821, "b": 203.22357, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "IBM Research", "bbox": {"l": 360.02002, "t": 205.40923999999995, "r": 416.12939, "b": 214.71429, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Rueschlikon, Switzerland", "bbox": {"l": 336.69302, "t": 217.36425999999994, "r": 439.45727999999997, "b": 226.66931, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "taa@zurich.ibm.com", "bbox": {"l": 346.20703, "t": 229.32025, "r": 429.94269, "b": 238.62531, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "ABSTRACT", "bbox": {"l": 53.798035, "t": 247.70288000000005, "r": 111.94354, "b": 258.01202, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Accurate document layout analysis is a key requirement for high-", "bbox": {"l": 53.484001, "t": 262.90454, "r": 295.55591, "b": 271.27917, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "quality PDF document conversion. With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}, {"id": 409, "text": "Figure 1: Four examples of complex page layouts across dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}, {"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}, {"id": 412, "text": "PDF document conversion, layout segmentation, object-detection,", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 616.04218, "coord_origin": "TOPLEFT"}}, {"id": 413, "text": "data set, Machine Learning", "bbox": {"l": 317.95499, "t": 618.62656, "r": 416.94403, "b": 627.00117, "coord_origin": "TOPLEFT"}}, {"id": 414, "text": "ACM Reference Format:", "bbox": {"l": 317.65997, "t": 640.05434, "r": 404.65366, "b": 647.58609, "coord_origin": "TOPLEFT"}}, {"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}, {"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "section_header", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}, "confidence": 0.8416302800178528, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for", "bbox": {"l": 107.29999999999998, "t": 83.69470000000013, "r": 505.06195, "b": 99.67058999999995, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Document-Layout Analysis", "bbox": {"l": 200.117, "t": 103.6196900000001, "r": 411.88367, "b": 119.59558000000015, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 90.96701, "t": 133.67236000000003, "r": 193.73123, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8637151718139648, "cells": [{"id": 2, "text": "Birgit Pfitzmann", "bbox": {"l": 102.06001, "t": 133.67236000000003, "r": 182.63805, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "IBM Research", "bbox": {"l": 114.29401000000001, "t": 147.02423, "r": 170.40337, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Rueschlikon, Switzerland", "bbox": {"l": 90.96701, "t": 158.97924999999998, "r": 193.73123, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "bpf@zurich.ibm.com", "bbox": {"l": 100.02301, "t": 170.93524000000002, "r": 184.67522, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 255.11602999999997, "t": 133.67236000000003, "r": 357.88025, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8612664937973022, "cells": [{"id": 6, "text": "Christoph Auer", "bbox": {"l": 268.62402, "t": 133.67236000000003, "r": 344.59933, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "IBM Research", "bbox": {"l": 278.44302, "t": 147.02423, "r": 334.55237, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rueschlikon, Switzerland", "bbox": {"l": 255.11602999999997, "t": 158.97924999999998, "r": 357.88025, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "cau@zurich.ibm.com", "bbox": {"l": 263.70404, "t": 170.93524000000002, "r": 349.29272, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 419.26505, "t": 133.67236000000003, "r": 522.0293, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8760607242584229, "cells": [{"id": 10, "text": "Michele Dolfi", "bbox": {"l": 437.6930500000001, "t": 133.67236000000003, "r": 503.60208, "b": 144.83856000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "IBM Research", "bbox": {"l": 442.59305000000006, "t": 147.02423, "r": 498.7023899999999, "b": 156.32928000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Rueschlikon, Switzerland", "bbox": {"l": 419.26505, "t": 158.97924999999998, "r": 522.0293, "b": 168.28430000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "dol@zurich.ibm.com", "bbox": {"l": 428.56104000000005, "t": 170.93524000000002, "r": 512.73505, "b": 180.24030000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.54303, "t": 192.05737, "r": 275.30725, "b": 238.62531, "coord_origin": "TOPLEFT"}, "confidence": 0.8319916129112244, "cells": [{"id": 14, "text": "Ahmed S. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107349038124084, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 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327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107349038124084, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.509588360786438, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910208821296692, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. 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With the recent availability of", "bbox": {"l": 53.79800000000001, "t": 273.86352999999997, "r": 294.04199, "b": 282.23816, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "bbox": {"l": 53.79800000000001, "t": 284.82254, "r": 295.34586, "b": 293.19717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "deep-learning models have proven to be very effective at layout", "bbox": {"l": 53.79800000000001, "t": 295.78152, "r": 294.04709, "b": 304.15616000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "detection and segmentation. While these datasets are of adequate", "bbox": {"l": 53.79800000000001, "t": 306.74053999999995, "r": 294.04645, "b": 315.11517, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "size to train such models, they severely lack in layout variability", "bbox": {"l": 53.79800000000001, "t": 317.69952, "r": 294.27573, "b": 326.07416, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "since they are sourced from scientific article repositories such as", "bbox": {"l": 53.79800000000001, "t": 328.65854, "r": 294.04712, "b": 337.03317, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "bbox": {"l": 53.79800000000001, "t": 339.61755, "r": 294.0437, "b": 347.99219, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "segmentation drops significantly when these models are applied", "bbox": {"l": 53.79800000000001, "t": 350.57654, "r": 294.04715, "b": 358.95117, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "on more challenging and diverse layouts. In this paper, we present", "bbox": {"l": 53.79800000000001, "t": 361.53455, "r": 294.04364, "b": 369.90918000000005, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DocLayNet", "bbox": {"l": 53.79800000000001, "t": 372.53839, "r": 92.863388, "b": 380.87714000000005, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ", a new, publicly available, document-layout annotation", "bbox": {"l": 92.863998, "t": 372.49353, "r": 294.04361, "b": 380.86816, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "dataset in COCO format. It contains 80863 manually annotated", "bbox": {"l": 53.79800000000001, "t": 383.45255, "r": 294.04718, "b": 391.82718, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "pages from diverse data sources to represent a wide variability in", "bbox": {"l": 53.79800000000001, "t": 394.41153, "r": 294.0437, "b": 402.78616, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "layouts. For each PDF page, the layout annotations provide labelled", "bbox": {"l": 53.79800000000001, "t": 405.37054, "r": 294.04535, "b": 413.74518, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "bbox": {"l": 53.79800000000001, "t": 416.32953, "r": 294.04715, "b": 424.70416000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "also provides a subset of double- and triple-annotated pages to", "bbox": {"l": 53.79800000000001, "t": 427.28853999999995, "r": 294.04712, "b": 435.66318, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "determine the inter-annotator agreement. In multiple experiments,", "bbox": {"l": 53.79800000000001, "t": 438.24753, "r": 295.03, "b": 446.62216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "bbox": {"l": 53.466999, "t": 449.20654, "r": 294.21616, "b": 457.58118, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "object detection models. We also demonstrate that these models", "bbox": {"l": 53.79800000000001, "t": 460.16553, "r": 294.04712, "b": 468.54016, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "bbox": {"l": 53.79800000000001, "t": 471.12354, "r": 295.56018, "b": 479.49817, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "bbox": {"l": 53.79800000000001, "t": 482.08255, "r": 295.42783, "b": 490.45718, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 493.04153, "r": 294.04715, "b": 501.41617, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "bbox": {"l": 53.79800000000001, "t": 504.00055, "r": 295.55618, "b": 512.37518, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "trained models are more robust and thus the preferred choice for", "bbox": {"l": 53.79800000000001, "t": 514.95953, "r": 294.21643, "b": 523.33417, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "general-purpose document-layout analysis.", "bbox": {"l": 53.79800000000001, "t": 525.91855, "r": 212.05495, "b": 534.29318, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"id": 49, "text": "CCS CONCEPTS", "bbox": {"l": 53.79800000000001, "t": 550.99692, "r": 134.81989, "b": 561.30602, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.797989, "t": 566.08299, "r": 297.85294, "b": 596.50114, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"id": 50, "text": "\u2022", "bbox": {"l": 53.79800000000001, "t": 566.19957, "r": 56.945206000000006, "b": 574.57419, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Information systems", "bbox": {"l": 58.440002, "t": 566.0830100000001, "r": 142.4462, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2192", "bbox": {"l": 143.938, "t": 566.36096, "r": 153.15099, "b": 574.43073, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Document structure", "bbox": {"l": 154.646, "t": 566.0830100000001, "r": 235.46015999999997, "b": 574.5562600000001, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "; \u2022", "bbox": {"l": 235.45700000000002, "t": 566.19955, "r": 242.17419, "b": 574.57417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Applied com-", "bbox": {"l": 243.66899, "t": 566.08299, "r": 297.85294, "b": 574.55624, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "puting", "bbox": {"l": 53.797989, "t": 577.0419899999999, "r": 80.661324, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "\u2192", "bbox": {"l": 83.565987, "t": 577.3199500000001, "r": 92.778961, "b": 585.38971, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Document analysis", "bbox": {"l": 95.68399, "t": 577.0419899999999, "r": 173.91583, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "; \u2022", "bbox": {"l": 173.916, "t": 577.15855, "r": 182.1272, "b": 585.53317, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Computing methodologies", "bbox": {"l": 185.032, "t": 577.0419899999999, "r": 294.0455, "b": 585.51524, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "\u2192", "bbox": {"l": 53.79800399999999, "t": 588.27895, "r": 63.01097899999999, "b": 596.34871, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Machine learning", "bbox": {"l": 65.253006, "t": 588.00099, "r": 136.80487, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": ";", "bbox": {"l": 136.80501, "t": 588.1175499999999, "r": 138.92108, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Computer vision", "bbox": {"l": 141.162, "t": 588.00099, "r": 209.60254, "b": 596.47424, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ";", "bbox": {"l": 209.60201, "t": 588.1175499999999, "r": 211.71808, "b": 596.49217, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Object detection", "bbox": {"l": 213.96001, "t": 588.16238, "r": 270.45728, "b": 596.50114, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": ";", "bbox": {"l": 270.48001, "t": 588.1175499999999, "r": 272.59607, "b": 596.49217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u2022 Information systems \u2192 Document structure ; \u2022 Applied computing \u2192 Document analysis ; \u2022 Computing methodologies \u2192 Machine learning ; Computer vision ; Object detection ;"}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 295.11798, "b": 672.79189, "coord_origin": "TOPLEFT"}, "confidence": 0.7107349038124084, "cells": [{"id": 68, "text": "Permission to make digital or hard copies of part or all of this work for personal or", "bbox": {"l": 53.79800000000001, "t": 634.39838, "r": 294.17697, "b": 640.9119000000001, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "classroom use is granted without fee provided that copies are not made or distributed", "bbox": {"l": 53.79800000000001, "t": 642.36838, "r": 294.04443, "b": 648.8819, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "bbox": {"l": 53.79800000000001, "t": 650.33838, "r": 294.04498, "b": 656.8519, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "bbox": {"l": 53.79800000000001, "t": 658.3083799999999, "r": 295.11798, "b": 664.8219, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "For all other uses, contact the owner/author(s).", "bbox": {"l": 53.79800000000001, "t": 666.27837, "r": 187.72285, "b": 672.79189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s)."}, {"label": "text", "id": 29, "page_no": 0, "cluster": {"id": 29, "label": "text", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 675.08023, "r": 197.86275, "b": 681.56586, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 30, "page_no": 0, "cluster": {"id": 30, "label": "text", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "bbox": {"l": 53.317001, "t": 683.81236, "r": 186.74652, "b": 690.32589, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "\u00a9 2022 Copyright held by the owner/author(s)."}, {"label": "text", "id": 31, "page_no": 0, "cluster": {"id": 31, "label": "text", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 75, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "bbox": {"l": 53.554001, "t": 691.78336, "r": 157.03125, "b": 698.29689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACM ISBN 978-1-4503-9385-0/22/08."}, {"label": "text", "id": 27, "page_no": 0, "cluster": {"id": 27, "label": "text", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}, "confidence": 0.509588360786438, "cells": [{"id": 76, "text": "https://doi.org/10.1145/3534678.3539043", "bbox": {"l": 53.79800000000001, "t": 699.753365, "r": 166.94093, "b": 706.266891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "https://doi.org/10.1145/3534678.3539043"}, {"label": "picture", "id": 20, "page_no": 0, "cluster": {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910208821296692, "cells": [{"id": 77, "text": "13", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "USING THE VERTICAL TUBE -", "bbox": {"l": 327.83005, "t": 331.57268999999997, "r": 351.16092, "b": 333.31171, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "MODELS AY11230/11234", "bbox": {"l": 327.83005, "t": 333.18292, "r": 348.30536, "b": 334.92194, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Make sure that both the images in", "bbox": {"l": 329.77588, "t": 346.84680000000003, "r": 351.18005, "b": 348.36847, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "OPERATION ", "bbox": {"l": 327.25311, "t": 254.94812000000002, "r": 350.07861, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "(", "bbox": {"l": 350.07861, "t": 254.76782000000003, "r": 351.82651, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "cont.", "bbox": {"l": 351.82651, "t": 254.94812000000002, "r": 360.85242, "b": 258.86096, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ")", "bbox": {"l": 360.85242, "t": 254.76782000000003, "r": 362.60028, "b": 258.68066, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "SELECTING OBJECTIVE ", "bbox": {"l": 326.88037, "t": 263.49492999999995, "r": 345.84351, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "MAGNIFICATION", "bbox": {"l": 326.88037, "t": 265.10515999999996, "r": 340.54153, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "1.", "bbox": {"l": 326.88037, "t": 266.71533, "r": 328.31903, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "There are two objectives. The lower", "bbox": {"l": 329.03836, "t": 266.71533, "r": 354.21472, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": " magnification objective has a greater", "bbox": {"l": 326.88037, "t": 268.32556, "r": 355.19193, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": " depth of field and view.", "bbox": {"l": 326.88037, "t": 269.93579, "r": 345.80057, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "2.", "bbox": {"l": 326.88037, "t": 271.54602, "r": 328.33862, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "In order to observe the specimen", "bbox": {"l": 329.06775, "t": 271.54602, "r": 352.39969, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": " easily use the lower magnification", "bbox": {"l": 326.88037, "t": 273.15619000000004, "r": 352.90042, "b": 274.89526, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": " objective first. Then, by rotating the", "bbox": {"l": 326.88037, "t": 274.76642000000004, "r": 354.59546, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": " case, the magnification can be", "bbox": {"l": 326.88037, "t": 276.37665000000004, "r": 350.81885, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": " changed.", "bbox": {"l": 326.88037, "t": 277.98688000000004, "r": 335.46707, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "CHANGING THE INTERPUPILLARY ", "bbox": {"l": 326.88037, "t": 281.20728, "r": 354.57755, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "DISTANCE", "bbox": {"l": 326.88037, "t": 282.81750000000005, "r": 335.1752, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "1.", "bbox": {"l": 326.88037, "t": 284.4277, "r": 328.34784, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "The distance between the observer's", "bbox": {"l": 329.08157, "t": 284.4277, "r": 354.76245, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": " pupils is the interpupillary distance.", "bbox": {"l": 326.88037, "t": 286.03793, "r": 354.6499, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "2.", "bbox": {"l": 326.88037, "t": 287.64813, "r": 328.25125, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "To adjust the interpupillary distance", "bbox": {"l": 328.93671, "t": 287.64813, "r": 354.29825, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": " rotate the prism caps until both eyes", "bbox": {"l": 326.88181, "t": 289.25836, "r": 355.02075, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": " coincide with the image in the", "bbox": {"l": 326.88181, "t": 290.86855999999995, "r": 350.82028, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": " eyepiece. ", "bbox": {"l": 326.88181, "t": 292.47879, "r": 336.2067, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "FOCUSING", "bbox": {"l": 326.88181, "t": 295.69922, "r": 335.3941, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "1.", "bbox": {"l": 326.88181, "t": 297.30942, "r": 328.34314, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Remove the lens protective cover.", "bbox": {"l": 329.07379, "t": 297.30942, "r": 353.18555, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "2.", "bbox": {"l": 326.88324, "t": 298.91965, "r": 328.35919, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "Place the specimen on the working", "bbox": {"l": 329.0972, "t": 298.91965, "r": 353.45065, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": " stage.", "bbox": {"l": 326.88324, "t": 300.52985, "r": 333.32825, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "3.", "bbox": {"l": 326.88324, "t": 302.14008000000007, "r": 328.31296, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "Focus the specimen with the left eye", "bbox": {"l": 329.02783, "t": 302.14008000000007, "r": 354.76303, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": " first while turning the focus knob until", "bbox": {"l": 326.88324, "t": 303.75027, "r": 355.96307, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": " the image appears clear and sharp.", "bbox": {"l": 326.88324, "t": 305.3605, "r": 354.46594, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "4.", "bbox": {"l": 326.88324, "t": 306.9707, "r": 328.25488, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Rotate the right eyepiece ring until the", "bbox": {"l": 328.9407, "t": 306.9707, "r": 356.37335, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": " images in each eyepiece coincide and", "bbox": {"l": 326.88324, "t": 308.58093, "r": 355.38867, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": " are sharp and clear.", "bbox": {"l": 326.88324, "t": 310.19113, "r": 343.17249, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "CHANGING THE BULB", "bbox": {"l": 326.88324, "t": 313.41156, "r": 344.13388, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "1.", "bbox": {"l": 326.88324, "t": 315.02178999999995, "r": 328.37418, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Disconnect the power cord.", "bbox": {"l": 329.11963, "t": 315.02178999999995, "r": 348.50162, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "2.", "bbox": {"l": 326.88324, "t": 316.63199, "r": 328.34061, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "When the bulb is cool, remove the", "bbox": {"l": 329.06931, "t": 316.63199, "r": 353.11588, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": " oblique illuminator cap and remove", "bbox": {"l": 326.88464, "t": 318.2422199999999, "r": 353.79517, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": " the halogen bulb with cap.", "bbox": {"l": 326.88464, "t": 319.85242000000005, "r": 348.02094, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "3.", "bbox": {"l": 326.88464, "t": 321.46265, "r": 328.37512, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Replace with a new halogen bulb.", "bbox": {"l": 329.12036, "t": 321.46265, "r": 352.96808, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "4.", "bbox": {"l": 326.88608, "t": 323.07285, "r": 328.36884, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Open the window in the base plate and", "bbox": {"l": 329.1102, "t": 323.07285, "r": 356.5412, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": " replace the halogen lamp or ", "bbox": {"l": 326.88608, "t": 324.68307000000004, "r": 350.13828, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": " fluorescent lamp of transmitted", "bbox": {"l": 326.88608, "t": 326.29327, "r": 351.59677, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": " illuminator.", "bbox": {"l": 326.88608, "t": 327.9035, "r": 336.89197, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "FOCUSING", "bbox": {"l": 358.42023, "t": 263.49492999999995, "r": 366.93256, "b": 265.23395000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "1.", "bbox": {"l": 358.42023, "t": 265.10515999999996, "r": 359.89841, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Turn the focusing knob away or toward", "bbox": {"l": 360.63751, "t": 265.10515999999996, "r": 387.98407, "b": 266.84418000000005, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": " you until a clear image is viewed.", "bbox": {"l": 358.42023, "t": 266.71533, "r": 384.58948, "b": 268.45441000000005, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "2.", "bbox": {"l": 358.42166, "t": 268.32556, "r": 359.78549, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "If the image is unclear, adjust the", "bbox": {"l": 360.46741, "t": 268.32556, "r": 384.33441, "b": 270.06458, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": " height of the elevator up or down,", "bbox": {"l": 358.4231, "t": 269.93579, "r": 384.61502, "b": 271.6748, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": " then turn the focusing knob again.", "bbox": {"l": 358.4231, "t": 271.54602, "r": 385.38922, "b": 273.28503, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "ZOOM MAGNIFICATION", "bbox": {"l": 358.4231, "t": 274.76642000000004, "r": 377.35046, "b": 276.50543000000005, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "1.", "bbox": {"l": 358.4231, "t": 276.37665000000004, "r": 359.89429, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "Turn the zoom magnification knob to", "bbox": {"l": 360.62988, "t": 276.37665000000004, "r": 386.37589, "b": 278.11566000000005, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": " the desired magnification and field of", "bbox": {"l": 358.4231, "t": 277.98688000000004, "r": 386.78732, "b": 279.72589000000005, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": " view.", "bbox": {"l": 358.4231, "t": 279.59704999999997, "r": 364.16855, "b": 281.33609, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "2.", "bbox": {"l": 358.4231, "t": 281.20728, "r": 359.86777, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "In most situations, it is recommended", "bbox": {"l": 360.59012, "t": 281.20728, "r": 387.31656, "b": 282.94632, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": " that you focus at the lowest ", "bbox": {"l": 358.4231, "t": 282.81750000000005, "r": 381.56656, "b": 284.55652, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": " magnification, then move to a higher", "bbox": {"l": 358.4231, "t": 284.4277, "r": 386.63403, "b": 286.16675, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": " magnification and re-focus as ", "bbox": {"l": 358.42453, "t": 286.03793, "r": 382.77115, "b": 287.77695, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": " necessary.", "bbox": {"l": 358.42453, "t": 287.64813, "r": 367.98694, "b": 289.38718, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "3.", "bbox": {"l": 358.42453, "t": 289.25836, "r": 359.80386, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "If the image is not clear to both eyes", "bbox": {"l": 360.49353, "t": 289.25836, "r": 386.70093, "b": 290.99738, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": " at the same time, the diopter ring may", "bbox": {"l": 358.42453, "t": 290.86855999999995, "r": 388.03534, "b": 292.6076, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": " need adjustment.", "bbox": {"l": 358.42453, "t": 292.47879, "r": 373.13724, "b": 294.2178, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "DIOPTER RING ADJUSTMENT", "bbox": {"l": 358.42453, "t": 295.69922, "r": 381.74539, "b": 297.43823, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "1.", "bbox": {"l": 358.42453, "t": 297.30942, "r": 359.83682, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "To adjust the eyepiece for viewing with", "bbox": {"l": 360.54297, "t": 297.30942, "r": 388.08289, "b": 299.04846, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": " or without eyeglasses and for ", "bbox": {"l": 358.42453, "t": 298.91965, "r": 382.73251, "b": 300.65866, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": " differences in acuity between the right", "bbox": {"l": 358.42453, "t": 300.52985, "r": 387.72266, "b": 302.26889000000006, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": " and left eyes, follow the following", "bbox": {"l": 358.42453, "t": 302.14008000000007, "r": 384.1991, "b": 303.8790900000001, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": " steps:", "bbox": {"l": 358.42453, "t": 303.75027, "r": 364.88672, "b": 305.48932, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "a.", "bbox": {"l": 358.42453, "t": 305.3605, "r": 359.95078, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Observe an image through the left", "bbox": {"l": 361.47699, "t": 305.3605, "r": 386.65988, "b": 307.09952000000004, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": " eyepiece and bring a specific point", "bbox": {"l": 358.42453, "t": 306.9707, "r": 386.7634, "b": 308.70975, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": " into focus using the focus knob.", "bbox": {"l": 358.42453, "t": 308.58093, "r": 385.41354, "b": 310.31995, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "b.", "bbox": {"l": 358.42453, "t": 310.19113, "r": 359.93304, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "By turning the diopter ring ", "bbox": {"l": 361.44156, "t": 310.19113, "r": 382.56085, "b": 311.93018, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": " adjustment for the left eyepiece,", "bbox": {"l": 358.42596, "t": 311.80136, "r": 385.4559, "b": 313.54037, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": " bring the same point into sharp", "bbox": {"l": 358.42596, "t": 313.41156, "r": 384.56122, "b": 315.15059999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": " focus.", "bbox": {"l": 358.42596, "t": 315.02178999999995, "r": 366.74371, "b": 316.76079999999996, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": " c.Then bring the same point into", "bbox": {"l": 358.42596, "t": 316.63199, "r": 383.93884, "b": 318.37103, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": " focus through the right eyepiece", "bbox": {"l": 358.42596, "t": 318.2422199999999, "r": 385.69241, "b": 319.9812299999999, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": " by turning the right diopter ring.", "bbox": {"l": 358.42596, "t": 319.85242000000005, "r": 385.94861, "b": 321.59146, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": " d.With more than one viewer, each", "bbox": {"l": 358.42596, "t": 321.46265, "r": 385.54236, "b": 323.20166, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": " viewer should note their own", "bbox": {"l": 358.42596, "t": 323.07285, "r": 382.98718, "b": 324.81189, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": " diopter ring position for the left", "bbox": {"l": 358.42596, "t": 324.68307000000004, "r": 385.06448, "b": 326.42209, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": " and right eyepieces, then before", "bbox": {"l": 358.42596, "t": 326.29327, "r": 385.20682, "b": 328.03232, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": " viewing set the diopter ring", "bbox": {"l": 358.42596, "t": 327.9035, "r": 382.21964, "b": 329.64252, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": " adjustments to that setting.", "bbox": {"l": 358.42596, "t": 329.5137, "r": 382.63382, "b": 331.25275, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "CHANGING THE BULB", "bbox": {"l": 358.42596, "t": 332.73412999999994, "r": 375.67661, "b": 334.47317999999996, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "1.", "bbox": {"l": 358.42596, "t": 334.34436, "r": 359.90311, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Disconnect the power cord from the", "bbox": {"l": 360.64169, "t": 334.34436, "r": 385.75333, "b": 336.08337, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": " electrical outlet.", "bbox": {"l": 358.42596, "t": 335.95456, "r": 372.01416, "b": 337.6936, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "2.", "bbox": {"l": 358.42596, "t": 337.56479, "r": 359.88327, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "When the bulb is cool, remove the", "bbox": {"l": 360.61191, "t": 337.56479, "r": 384.65726, "b": 339.3038, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": " oblique illuminator cap and remove", "bbox": {"l": 358.42596, "t": 339.17499, "r": 385.33649, "b": 340.9140300000001, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": " the halogen bulb with cap.", "bbox": {"l": 358.42596, "t": 340.78522, "r": 379.57224, "b": 342.52423, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "3.", "bbox": {"l": 358.4274, "t": 342.39542, "r": 359.91788, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Replace with a new halogen bulb.", "bbox": {"l": 360.66312, "t": 342.39542, "r": 384.5108, "b": 344.13446000000005, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "4.", "bbox": {"l": 358.42883, "t": 344.00565000000006, "r": 359.92792, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Open the window in the base plate", "bbox": {"l": 360.67746, "t": 344.00565000000006, "r": 385.41235, "b": 345.74466, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": " and replace the halogen lamp or", "bbox": {"l": 358.42883, "t": 345.61584, "r": 383.2782, "b": 347.35489, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": " fluorescent lamp of transmitted", "bbox": {"l": 358.42883, "t": 347.22607, "r": 383.13953, "b": 348.96509, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": " illuminator.", "bbox": {"l": 358.42883, "t": 348.83627, "r": 368.43472, "b": 350.57532, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Model AY11230", "bbox": {"l": 326.59567, "t": 261.14185, "r": 339.11377, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "Model AY11234", "bbox": {"l": 358.48605, "t": 261.14185, "r": 371.00415, "b": 262.88091999999995, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "14", "bbox": {"l": 455.43533, "t": 351.77038999999996, "r": 457.97827000000007, "b": 353.94415, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Objectives", "bbox": {"l": 408.24518, "t": 275.52673000000004, "r": 414.4234, "b": 276.96020999999996, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "Revolving Turret", "bbox": {"l": 409.39554, "t": 268.98235999999997, "r": 419.06677, "b": 270.41583, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Coarse ", "bbox": {"l": 441.3895, "t": 279.12627999999995, "r": 445.87192, "b": 280.55975, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": "Adjustment", "bbox": {"l": 441.3895, "t": 280.30609, "r": 448.22338999999994, "b": 281.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Knob", "bbox": {"l": 441.3895, "t": 281.48593, "r": 444.40371999999996, "b": 282.91939999999994, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": "MODEL AY11236", "bbox": {"l": 398.79288, "t": 254.94646999999998, "r": 428.91568, "b": 258.85931000000005, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "MICROSCOPE USAGE", "bbox": {"l": 398.32535, "t": 305.04291, "r": 435.93542, "b": 308.95572000000004, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": "BARSKA Model AY11236 is a powerful fixed power compound ", "bbox": {"l": 398.08594, "t": 310.35892, "r": 453.72171, "b": 312.53271, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "microscope designed for biological studies such as specimen ", "bbox": {"l": 398.08594, "t": 312.50586, "r": 453.09939999999995, "b": 314.67966, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": "examination. It can also be used for examining bacteria and", "bbox": {"l": 398.08594, "t": 314.6528, "r": 456.65246999999994, "b": 316.8266, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "for general clinical and medical studies and other scientific uses. ", "bbox": {"l": 398.08594, "t": 316.79977, "r": 456.73859000000004, "b": 318.97354, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": "CONSTRUCTION", "bbox": {"l": 398.62399, "t": 320.42941, "r": 427.77472, "b": 324.34222000000005, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "bbox": {"l": 398.08594, "t": 326.46069000000006, "r": 456.02639999999997, "b": 328.63449, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "It is constructed with two optical paths at the same angle. It is ", "bbox": {"l": 398.08414, "t": 328.6076699999999, "r": 455.42238999999995, "b": 330.7814599999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "equipped with transmitted illumination. By using this instrument, ", "bbox": {"l": 398.08414, "t": 330.75461, "r": 457.39844, "b": 332.92841, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "the user can observe specimens at magnification from 40x to ", "bbox": {"l": 398.08414, "t": 332.90155, "r": 453.97745, "b": 335.07535000000007, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "1000x by selecting the desired objective lens. Coarse and fine ", "bbox": {"l": 398.08414, "t": 335.04852, "r": 454.70708999999994, "b": 337.22232, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "focus adjustments provide accuracy and image detail. The rotating ", "bbox": {"l": 398.08414, "t": 337.19547, "r": 458.90240000000006, "b": 339.36926, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "head allows the user to position the eyepieces for maximum ", "bbox": {"l": 398.08594, "t": 339.34241, "r": 453.0672, "b": 341.5162, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "viewing comfort and easy access to all adjustment knobs.", "bbox": {"l": 398.08594, "t": 341.48938, "r": 449.63113, "b": 343.66318, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Model AY11236", "bbox": {"l": 422.10626, "t": 301.24191, "r": 434.62433000000004, "b": 302.98096, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "Fine ", "bbox": {"l": 442.01610999999997, "t": 283.08649, "r": 444.8817399999999, "b": 284.51996, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Adjustment", "bbox": {"l": 442.01610999999997, "t": 284.2663, "r": 448.85001, "b": 285.69980000000004, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "Knob", "bbox": {"l": 442.01610999999997, "t": 285.44611, "r": 445.03033000000005, "b": 286.87961, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "Stage", "bbox": {"l": 408.00577, "t": 279.12579000000005, "r": 411.42212, "b": 280.5593, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Condenser ", "bbox": {"l": 404.07172, "t": 280.9144299999999, "r": 410.77707, "b": 282.3479, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "Focusing", "bbox": {"l": 404.07172, "t": 282.09424, "r": 409.2157, "b": 283.52774, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Knob", "bbox": {"l": 404.07172, "t": 283.27408, "r": 407.08594, "b": 284.7075500000001, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": "Eyepiece", "bbox": {"l": 441.81281, "t": 262.32178, "r": 447.03702, "b": 263.75525000000005, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "Stand", "bbox": {"l": 437.34607, "t": 271.13025000000005, "r": 440.80496, "b": 272.56281, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "Lamp ", "bbox": {"l": 409.7164, "t": 284.40027, "r": 413.3768, "b": 285.83282, "coord_origin": "TOPLEFT"}}, {"id": 242, "text": "On/Off", "bbox": {"l": 409.7164, "t": 285.83163, "r": 413.68201, "b": 287.26416, "coord_origin": "TOPLEFT"}}, {"id": 243, "text": "Switch", "bbox": {"l": 409.7164, "t": 287.263, "r": 413.6337, "b": 288.69553, "coord_origin": "TOPLEFT"}}, {"id": 244, "text": "Lamp ", "bbox": {"l": 434.8712499999999, "t": 296.7153, "r": 438.53164999999996, "b": 298.14783, "coord_origin": "TOPLEFT"}}, {"id": 245, "text": "Power", "bbox": {"l": 439.52039, "t": 292.18307000000004, "r": 443.08768, "b": 293.61560000000003, "coord_origin": "TOPLEFT"}}, {"id": 246, "text": "Cord", "bbox": {"l": 439.52039, "t": 293.61444, "r": 442.29575, "b": 295.04697, "coord_origin": "TOPLEFT"}}, {"id": 247, "text": "Rotating Head", "bbox": {"l": 413.55829, "t": 264.66089, "r": 421.94913, "b": 266.09344, "coord_origin": "TOPLEFT"}}, {"id": 248, "text": "Stage Clip", "bbox": {"l": 441.84316999999993, "t": 286.90573, "r": 447.87585000000007, "b": 288.33826, "coord_origin": "TOPLEFT"}}, {"id": 249, "text": "Adjustment", "bbox": {"l": 441.84316999999993, "t": 288.3371, "r": 448.67252, "b": 289.76962000000003, "coord_origin": "TOPLEFT"}}, {"id": 250, "text": "Interpupillary Slide Adjustment", "bbox": {"l": 407.2403, "t": 259.86645999999996, "r": 425.79089, "b": 261.29895, "coord_origin": "TOPLEFT"}}, {"id": 251, "text": "Circling Minimums", "bbox": {"l": 449.10074000000003, "t": 378.66302, "r": 466.08835000000005, "b": 380.78412, "coord_origin": "TOPLEFT"}}, {"id": 252, "text": "7", "bbox": {"l": 449.10074000000003, "t": 383.2203999999999, "r": 449.64444, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 253, "text": "K H U H Z D V D F K D Q J H W R W K H 7 ( 5 3 6 F U L W H U L D L Q W K D W D \u1087H F W V F L U F O L Q J D U H D G L P H Q V L R Q E \\ H [ S D Q G L Q J W K H D U H D V W R S U R Y L G H ", "bbox": {"l": 450.18811, "t": 383.2203999999999, "r": 550.77124, "b": 385.34148999999996, "coord_origin": "TOPLEFT"}}, {"id": 254, "text": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "bbox": {"l": 449.10074000000003, "t": 385.75732, "r": 536.14716, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 255, "text": " is placed on ", "bbox": {"l": 538.31085, "t": 385.75732, "r": 549.49921, "b": 387.87842, "coord_origin": "TOPLEFT"}}, {"id": 256, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "bbox": {"l": 449.10074000000003, "t": 388.03601, "r": 547.58185, "b": 390.1571, "coord_origin": "TOPLEFT"}}, {"id": 257, "text": "7", "bbox": {"l": 449.10074000000003, "t": 393.2128000000001, "r": 449.6163, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 258, "text": "K H D S S U R D F K H V X V L Q J V W D Q G D U G F L U F O L Q J D S S U R D F K D U H D V F D Q E H L G H Q W L \u00bf H G E \\ W K H D E V H Q F H R I W K H ", "bbox": {"l": 450.1319, "t": 393.2128000000001, "r": 529.53082, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 259, "text": " on the circling line of ", "bbox": {"l": 532.05829, "t": 393.2128000000001, "r": 550.42261, "b": 395.33386, "coord_origin": "TOPLEFT"}}, {"id": 260, "text": "minima.", "bbox": {"l": 449.10074000000003, "t": 395.49149, "r": 455.74692, "b": 397.61255, "coord_origin": "TOPLEFT"}}, {"id": 261, "text": "$ S S O \\ 6 W D Q G D U G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J 5 D G L X V 7 D E O H ", "bbox": {"l": 449.95525999999995, "t": 415.59549, "r": 496.2829, "b": 417.50446, "coord_origin": "TOPLEFT"}}, {"id": 262, "text": "$ S S O \\ ( [ S D Q G H G & L U F O L Q J $ S S U R D F K 0 D Q H X Y H U L Q J $ L U V S D F H 5 D G L X V ", "bbox": {"l": 501.13077, "t": 409.25543, "r": 551.16101, "b": 411.1644, "coord_origin": "TOPLEFT"}}, {"id": 263, "text": "Table", "bbox": {"l": 501.13077, "t": 411.30624, "r": 505.2477999999999, "b": 413.21521, "coord_origin": "TOPLEFT"}}, {"id": 264, "text": "AIRPORT SKETCH", "bbox": {"l": 449.10074000000003, "t": 420.18802, "r": 469.35599, "b": 422.73331, "coord_origin": "TOPLEFT"}}, {"id": 265, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "bbox": {"l": 449.10074000000003, "t": 425.08908, "r": 525.93616, "b": 427.21017, "coord_origin": "TOPLEFT"}}, {"id": 266, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "bbox": {"l": 449.10074000000003, "t": 427.3678, "r": 522.0343, "b": 429.48886, "coord_origin": "TOPLEFT"}}, {"id": 267, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "bbox": {"l": 449.10074000000003, "t": 429.64648, "r": 524.67151, "b": 431.76755, "coord_origin": "TOPLEFT"}}, {"id": 268, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "bbox": {"l": 449.10074000000003, "t": 431.92514000000006, "r": 527.172, "b": 434.04623, "coord_origin": "TOPLEFT"}}, {"id": 269, "text": "dimensions (length and width) are shown for all active runways.", "bbox": {"l": 449.10074000000003, "t": 434.20383, "r": 502.39545, "b": 436.32492, "coord_origin": "TOPLEFT"}}, {"id": 270, "text": "Runway(s) are depicted based on what type and construction of the runway.", "bbox": {"l": 449.10074000000003, "t": 438.7611999999999, "r": 512.92676, "b": 440.88228999999995, "coord_origin": "TOPLEFT"}}, {"id": 271, "text": "Hard Surface", "bbox": {"l": 449.95525999999995, "t": 444.07001, "r": 460.02307, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 272, "text": "Other Than ", "bbox": {"l": 464.89963, "t": 444.07001, "r": 473.98819, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 273, "text": "Hard Surface", "bbox": {"l": 464.89963, "t": 446.12085, "r": 474.96744, "b": 448.02979, "coord_origin": "TOPLEFT"}}, {"id": 274, "text": "Metal Surface", "bbox": {"l": 478.91357, "t": 444.07001, "r": 489.45648, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 275, "text": "Closed Runway", "bbox": {"l": 493.06420999999995, "t": 444.07001, "r": 505.03076, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 276, "text": "Under Construction", "bbox": {"l": 509.5809, "t": 444.07001, "r": 524.30237, "b": 445.97900000000004, "coord_origin": "TOPLEFT"}}, {"id": 277, "text": "Stopways, ", "bbox": {"l": 449.95525999999995, "t": 454.81207, "r": 458.31406, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 278, "text": "Taxiways, Park-", "bbox": {"l": 449.95525999999995, "t": 456.86288, "r": 461.92083999999994, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 279, "text": "ing Areas", "bbox": {"l": 449.95525999999995, "t": 458.91373, "r": 457.08014, "b": 460.82268999999997, "coord_origin": "TOPLEFT"}}, {"id": 280, "text": "Displaced ", "bbox": {"l": 464.89963, "t": 454.81207, "r": 472.87732, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 281, "text": "Threshold", "bbox": {"l": 464.89963, "t": 456.86288, "r": 472.49792, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 282, "text": "Closed", "bbox": {"l": 478.91357, "t": 454.81207, "r": 483.61584, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 283, "text": "Pavement", "bbox": {"l": 478.91357, "t": 456.86288, "r": 486.60754000000003, "b": 458.77185000000003, "coord_origin": "TOPLEFT"}}, {"id": 284, "text": "Water Runway", "bbox": {"l": 493.06420999999995, "t": 454.81207, "r": 504.20648, "b": 456.72104, "coord_origin": "TOPLEFT"}}, {"id": 285, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "bbox": {"l": 449.10074000000003, "t": 469.32974, "r": 548.59674, "b": 471.45081, "coord_origin": "TOPLEFT"}}, {"id": 286, "text": "sions, runway slope, arresting gear, and displaced threshold.", "bbox": {"l": 449.10074000000003, "t": 471.60843, "r": 500.08181999999994, "b": 473.72949, "coord_origin": "TOPLEFT"}}, {"id": 287, "text": "2", "bbox": {"l": 449.10074000000003, "t": 476.16577, "r": 449.59933000000007, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 288, "text": "W K H U L Q I R U P D W L R Q F R Q F H U Q L Q J O L J K W L Q J \u00bf Q D O D S S U R D F K E H D U L Q J V D L U S R U W E H D F R Q R E V W D F O H V F R Q W U R O W R Z H U 1 $ 9 $ , ' V K H O L ", "bbox": {"l": 450.09796, "t": 476.16577, "r": 547.82562, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 289, "text": "-", "bbox": {"l": 547.82623, "t": 476.16577, "r": 548.45862, "b": 478.28687, "coord_origin": "TOPLEFT"}}, {"id": 290, "text": "pads may also be shown.", "bbox": {"l": 449.10074000000003, "t": 478.44446, "r": 470.52609000000007, "b": 480.56555, "coord_origin": "TOPLEFT"}}, {"id": 291, "text": "$ L U S R U W ( O H Y D W L R Q D Q G 7 R X F K G R Z Q = R Q H ( O H Y D W L R Q ", "bbox": {"l": 449.10074000000003, "t": 483.00183, "r": 493.37906000000004, "b": 485.12292, "coord_origin": "TOPLEFT"}}, {"id": 292, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "bbox": {"l": 449.10074000000003, "t": 487.5592, "r": 549.16168, "b": 489.6803, "coord_origin": "TOPLEFT"}}, {"id": 293, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "bbox": {"l": 449.10074000000003, "t": 489.83789, "r": 546.90881, "b": 491.95898, "coord_origin": "TOPLEFT"}}, {"id": 294, "text": "D L U S R U W \u00b6 V X V D E O H U X Q Z D \\ V P H D V X U H G L Q I H H W I U R P P H D Q V H D O H Y H O 7 K H 7 ' = ( L V W K H K L J K H V W H O H Y D W L R Q L Q W K H \u00bf U V W I H H W R I ", "bbox": {"l": 449.10074000000003, "t": 492.11658, "r": 551.80023, "b": 494.23767, "coord_origin": "TOPLEFT"}}, {"id": 295, "text": "the landing surface. Circling only approaches will not show a TDZE.", "bbox": {"l": 449.10074000000003, "t": 494.39526, "r": 505.85068000000007, "b": 496.51636, "coord_origin": "TOPLEFT"}}, {"id": 296, "text": "114", "bbox": {"l": 498.80661000000003, "t": 515.9437, "r": 502.08792, "b": 519.01764, "coord_origin": "TOPLEFT"}}, {"id": 297, "text": "FAA Chart Users\u2019 Guide - Terminal Procedures Publication (TPP) - Terms", "bbox": {"l": 444.56319999999994, "t": 422.84869, "r": 446.25998, "b": 471.87128, "coord_origin": "TOPLEFT"}}, {"id": 298, "text": "AGL 2013 Financial Calendar", "bbox": {"l": 329.40536, "t": 379.37537, "r": 355.13138, "b": 382.13336, "coord_origin": "TOPLEFT"}}, {"id": 299, "text": "22", "bbox": {"l": 329.40536, "t": 382.30273, "r": 330.96848, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 300, "text": "August 2012 ", "bbox": {"l": 331.75003, "t": 382.30273, "r": 341.12875, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 301, "text": "2012 full year result and fi nal dividend announced", "bbox": {"l": 350.4722, "t": 382.30273, "r": 384.81079, "b": 384.55927, "coord_origin": "TOPLEFT"}}, {"id": 302, "text": "30", "bbox": {"l": 329.40536, "t": 384.84552, "r": 330.97336, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 303, "text": "August 2012 ", "bbox": {"l": 331.75735, "t": 384.84552, "r": 341.16534, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 304, "text": "Ex-dividend trading commences", "bbox": {"l": 350.4722, "t": 384.84552, "r": 372.90613, "b": 387.10205, "coord_origin": "TOPLEFT"}}, {"id": 305, "text": "5", "bbox": {"l": 329.40536, "t": 387.38828, "r": 330.20337, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 306, "text": "September 2012 ", "bbox": {"l": 331.00137, "t": 387.38828, "r": 342.9715, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 307, "text": "Record date for 2012 fi nal dividend", "bbox": {"l": 350.4722, "t": 387.38828, "r": 374.88693, "b": 389.64483999999993, "coord_origin": "TOPLEFT"}}, {"id": 308, "text": "27", "bbox": {"l": 329.40536, "t": 389.93103, "r": 331.0173, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 309, "text": "September 2012 ", "bbox": {"l": 331.82327, "t": 389.93103, "r": 343.91284, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 310, "text": "Final dividend payable", "bbox": {"l": 350.4722, "t": 389.93103, "r": 365.65988, "b": 392.18762, "coord_origin": "TOPLEFT"}}, {"id": 311, "text": "23", "bbox": {"l": 329.40536, "t": 392.47382, "r": 330.98804, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 312, "text": "October 2012 ", "bbox": {"l": 331.77936, "t": 392.47382, "r": 342.06674, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 313, "text": "Annual General Meeting", "bbox": {"l": 350.4722, "t": 392.47382, "r": 367.22156, "b": 394.73037999999997, "coord_origin": "TOPLEFT"}}, {"id": 314, "text": "27", "bbox": {"l": 329.40536, "t": 395.0166, "r": 330.99741, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 315, "text": "February 2013", "bbox": {"l": 331.7934, "t": 395.0166, "r": 342.1416, "b": 397.27313, "coord_origin": "TOPLEFT"}}, {"id": 316, "text": " 1", "bbox": {"l": 342.64841, "t": 395.18298, "r": 342.65811, "b": 396.49857000000003, "coord_origin": "TOPLEFT"}}, {"id": 317, "text": "2013 interim result and interim dividend announced", "bbox": {"l": 350.47177, "t": 395.01474, "r": 386.25897, "b": 397.2713, "coord_origin": "TOPLEFT"}}, {"id": 318, "text": "28", "bbox": {"l": 329.40491, "t": 397.55749999999995, "r": 331.02695, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 319, "text": "August 2013", "bbox": {"l": 331.83795, "t": 397.55749999999995, "r": 340.75909, "b": 399.81406, "coord_origin": "TOPLEFT"}}, {"id": 320, "text": " 1", "bbox": {"l": 341.26437, "t": 397.7254, "r": 341.27408, "b": 399.04095, "coord_origin": "TOPLEFT"}}, {"id": 321, "text": "2013 full year results and fi nal dividend announced ", "bbox": {"l": 350.47144, "t": 397.55713, "r": 385.93265, "b": 399.81369, "coord_origin": "TOPLEFT"}}, {"id": 322, "text": "1", "bbox": {"l": 329.40536, "t": 400.46155, "r": 329.87708, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 323, "text": "Indicative dates only, subject to change/Board confi rmation", "bbox": {"l": 330.34882, "t": 400.46155, "r": 358.65204, "b": 401.96588, "coord_origin": "TOPLEFT"}}, {"id": 324, "text": "AGL\u2019s Annual General Meeting will be held at the City Recital Hall, Angel Place, Sydney ", "bbox": {"l": 329.40536, "t": 404.34503, "r": 391.771, "b": 406.60156, "coord_origin": "TOPLEFT"}}, {"id": 325, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "bbox": {"l": 329.40536, "t": 406.37857, "r": 369.65308, "b": 408.63513000000006, "coord_origin": "TOPLEFT"}}, {"id": 326, "text": "Ye s te rd ay", "bbox": {"l": 363.54486, "t": 460.53054999999995, "r": 379.25955, "b": 465.54507, "coord_origin": "TOPLEFT"}}, {"id": 327, "text": "Established in Sydney in 1837, and then ", "bbox": {"l": 363.54486, "t": 466.7157, "r": 391.38229, "b": 468.97223, "coord_origin": "TOPLEFT"}}, {"id": 328, "text": "known as The Australian Gas Light Company, ", "bbox": {"l": 363.54486, "t": 468.74924, "r": 395.01788, "b": 471.00577, "coord_origin": "TOPLEFT"}}, {"id": 329, "text": "the AGL business has an established history ", "bbox": {"l": 363.54486, "t": 470.78281, "r": 394.08322, "b": 473.03934, "coord_origin": "TOPLEFT"}}, {"id": 330, "text": "and reputation for serving the gas and ", "bbox": {"l": 363.54486, "t": 472.81635, "r": 390.60727, "b": 475.07288, "coord_origin": "TOPLEFT"}}, {"id": 331, "text": "electricity needs of Australian households. ", "bbox": {"l": 363.54486, "t": 474.84988, "r": 393.49612, "b": 477.10645, "coord_origin": "TOPLEFT"}}, {"id": 332, "text": "In 1841, when AGL supplied the gas to light ", "bbox": {"l": 363.54486, "t": 476.88345, "r": 394.11481, "b": 479.13998, "coord_origin": "TOPLEFT"}}, {"id": 333, "text": "the fi rst public street lamp, it was reported ", "bbox": {"l": 363.54486, "t": 478.91699, "r": 393.75891, "b": 481.17352, "coord_origin": "TOPLEFT"}}, {"id": 334, "text": "in the Sydney Gazette as a \u201cwonderful ", "bbox": {"l": 363.54486, "t": 480.95053, "r": 390.4975, "b": 483.20709, "coord_origin": "TOPLEFT"}}, {"id": 335, "text": "achievement of scientifi c knowledge, assisted ", "bbox": {"l": 363.54486, "t": 482.9841, "r": 395.70975, "b": 485.24063, "coord_origin": "TOPLEFT"}}, {"id": 336, "text": "by mechanical ingenuity.\u201d Within two years, ", "bbox": {"l": 363.54486, "t": 485.01764, "r": 394.27283, "b": 487.2742, "coord_origin": "TOPLEFT"}}, {"id": 337, "text": "165 gas lamps were lighting the City of Sydney.", "bbox": {"l": 363.54486, "t": 487.05121, "r": 396.65939, "b": 489.30774, "coord_origin": "TOPLEFT"}}, {"id": 338, "text": "Looking back on ", "bbox": {"l": 329.4054, "t": 419.93124, "r": 384.19696, "b": 431.09412, "coord_origin": "TOPLEFT"}}, {"id": 339, "text": "175 years of ", "bbox": {"l": 329.4054, "t": 430.10379, "r": 372.16626, "b": 441.26669, "coord_origin": "TOPLEFT"}}, {"id": 340, "text": "looking forward.", "bbox": {"l": 329.4054, "t": 440.27636999999993, "r": 385.3981, "b": 451.43924, "coord_origin": "TOPLEFT"}}, {"id": 341, "text": "AGL Energy Limited ABN 74 115 061 375", "bbox": {"l": 329.40536, "t": 372.16159, "r": 353.36179, "b": 373.91669, "coord_origin": "TOPLEFT"}}, {"id": 342, "text": "29", "bbox": {"l": 546.20587, "t": 360.90448, "r": 548.23407, "b": 362.82242, "coord_origin": "TOPLEFT"}}, {"id": 343, "text": "signs, signals and road markings", "bbox": {"l": 497.77728, "t": 251.43384000000003, "r": 542.8255, "b": 254.94385, "coord_origin": "TOPLEFT"}}, {"id": 344, "text": "3", "bbox": {"l": 490.30679, "t": 251.47478999999998, "r": 492.09982, "b": 254.98479999999995, "coord_origin": "TOPLEFT"}}, {"id": 345, "text": "In ", "bbox": {"l": 498.15335, "t": 263.88922, "r": 500.05637, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 346, "text": "chapter 2, you and your vehicle", "bbox": {"l": 500.05637, "t": 263.85717999999997, "r": 524.37036, "b": 265.86310000000003, "coord_origin": "TOPLEFT"}}, {"id": 347, "text": ", you learned about ", "bbox": {"l": 524.37036, "t": 263.88922, "r": 539.89124, "b": 265.92719, "coord_origin": "TOPLEFT"}}, {"id": 348, "text": "some of the controls in your vehicle. This chapter is a handy ", "bbox": {"l": 498.15335, "t": 265.93224999999995, "r": 544.50403, "b": 267.97020999999995, "coord_origin": "TOPLEFT"}}, {"id": 349, "text": "reference section that gives examples of the most common ", "bbox": {"l": 498.15335, "t": 267.97533999999996, "r": 544.01343, "b": 270.01331000000005, "coord_origin": "TOPLEFT"}}, {"id": 350, "text": "signs, signals and road markings that keep traffi c organized ", "bbox": {"l": 498.15335, "t": 270.01831000000004, "r": 544.11987, "b": 272.05634, "coord_origin": "TOPLEFT"}}, {"id": 351, "text": "and flowing smoothly. ", "bbox": {"l": 498.15335, "t": 272.06140000000005, "r": 515.41071, "b": 274.09937, "coord_origin": "TOPLEFT"}}, {"id": 352, "text": "Signs", "bbox": {"l": 498.15335, "t": 277.34619, "r": 505.64642000000003, "b": 280.9357, "coord_origin": "TOPLEFT"}}, {"id": 353, "text": "There are three ways to read signs: by their shape, colour and ", "bbox": {"l": 498.15335, "t": 281.82187, "r": 543.92957, "b": 283.85983, "coord_origin": "TOPLEFT"}}, {"id": 354, "text": "the messages printed on them. Understanding these three ways ", "bbox": {"l": 498.15335, "t": 283.8649, "r": 545.67834, "b": 285.90289, "coord_origin": "TOPLEFT"}}, {"id": 355, "text": "of classifying signs will help you figure out the meaning of signs ", "bbox": {"l": 498.15335, "t": 285.90796, "r": 545.26471, "b": 287.94592, "coord_origin": "TOPLEFT"}}, {"id": 356, "text": "that are new to you. ", "bbox": {"l": 498.15335, "t": 287.95099, "r": 513.31335, "b": 289.98895, "coord_origin": "TOPLEFT"}}, {"id": 357, "text": "Stop", "bbox": {"l": 505.43439, "t": 303.07596, "r": 508.53033000000005, "b": 304.89639, "coord_origin": "TOPLEFT"}}, {"id": 358, "text": "Yield the right-of-way", "bbox": {"l": 527.45502, "t": 303.25354, "r": 541.44678, "b": 305.07397, "coord_origin": "TOPLEFT"}}, {"id": 359, "text": "Shows driving", "bbox": {"l": 501.79385, "t": 321.18973, "r": 510.41632, "b": 323.01016, "coord_origin": "TOPLEFT"}}, {"id": 360, "text": "regulations", "bbox": {"l": 501.79385, "t": 322.87731999999994, "r": 509.04268999999994, "b": 324.69775000000004, "coord_origin": "TOPLEFT"}}, {"id": 361, "text": "Explains lane use", "bbox": {"l": 518.66455, "t": 319.59146, "r": 529.80902, "b": 321.41190000000006, "coord_origin": "TOPLEFT"}}, {"id": 362, "text": "School zone signs ", "bbox": {"l": 534.87561, "t": 318.37616, "r": 546.95142, "b": 320.19659, "coord_origin": "TOPLEFT"}}, {"id": 363, "text": "are fl uorescent ", "bbox": {"l": 534.87561, "t": 320.0637500000001, "r": 545.05762, "b": 321.88419, "coord_origin": "TOPLEFT"}}, {"id": 364, "text": "yellow-green", "bbox": {"l": 534.87561, "t": 321.75134, "r": 543.32263, "b": 323.57178, "coord_origin": "TOPLEFT"}}, {"id": 365, "text": "Tells about motorist ", "bbox": {"l": 499.21862999999996, "t": 338.12772, "r": 512.62451, "b": 339.94815, "coord_origin": "TOPLEFT"}}, {"id": 366, "text": "services", "bbox": {"l": 499.21862999999996, "t": 339.81531000000007, "r": 504.39917, "b": 341.63574, "coord_origin": "TOPLEFT"}}, {"id": 367, "text": "Shows a permitted ", "bbox": {"l": 516.97748, "t": 338.06039, "r": 529.77484, "b": 339.88082999999995, "coord_origin": "TOPLEFT"}}, {"id": 368, "text": "action", "bbox": {"l": 516.97748, "t": 339.74799, "r": 520.96399, "b": 341.56842, "coord_origin": "TOPLEFT"}}, {"id": 369, "text": "Shows an action that ", "bbox": {"l": 534.55847, "t": 337.88281, "r": 548.58453, "b": 339.7032500000001, "coord_origin": "TOPLEFT"}}, {"id": 370, "text": "is not permitted", "bbox": {"l": 534.55847, "t": 339.57040000000006, "r": 545.08862, "b": 341.39084, "coord_origin": "TOPLEFT"}}, {"id": 371, "text": "Warns of hazards ", "bbox": {"l": 483.05853, "t": 356.17416, "r": 494.72577, "b": 357.9946, "coord_origin": "TOPLEFT"}}, {"id": 372, "text": "ahead", "bbox": {"l": 483.05853, "t": 357.86179, "r": 487.07525999999996, "b": 359.68222, "coord_origin": "TOPLEFT"}}, {"id": 373, "text": "Warns of", "bbox": {"l": 499.39645, "t": 356.26297000000005, "r": 504.69171, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 374, "text": "construction zones", "bbox": {"l": 499.39645, "t": 357.95056, "r": 511.69116, "b": 359.77099999999996, "coord_origin": "TOPLEFT"}}, {"id": 375, "text": "Railway crossing", "bbox": {"l": 516.75891, "t": 356.26297000000005, "r": 527.42938, "b": 358.0834, "coord_origin": "TOPLEFT"}}, {"id": 376, "text": "Shows distance and ", "bbox": {"l": 534.5141, "t": 352.92981, "r": 547.89862, "b": 354.75024, "coord_origin": "TOPLEFT"}}, {"id": 377, "text": "direction", "bbox": {"l": 534.5141, "t": 354.6174, "r": 540.2818, "b": 356.43784, "coord_origin": "TOPLEFT"}}, {"id": 378, "text": "\u2022", "bbox": {"l": 478.37466, "t": 270.14075, "r": 479.14251999999993, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 379, "text": "Signs", "bbox": {"l": 479.91036999999994, "t": 270.14075, "r": 483.74963, "b": 272.17877, "coord_origin": "TOPLEFT"}}, {"id": 380, "text": "- regulatory signs", "bbox": {"l": 479.97293, "t": 272.84717, "r": 492.31219, "b": 274.34888, "coord_origin": "TOPLEFT"}}, {"id": 381, "text": "- school, ", "bbox": {"l": 479.97293, "t": 275.14513999999997, "r": 486.72598000000005, "b": 276.64679, "coord_origin": "TOPLEFT"}}, {"id": 382, "text": "playground and ", "bbox": {"l": 481.21602999999993, "t": 276.77972, "r": 492.93286000000006, "b": 278.81768999999997, "coord_origin": "TOPLEFT"}}, {"id": 383, "text": "crosswalk signs", "bbox": {"l": 481.21602999999993, "t": 278.82275000000004, "r": 491.82938000000007, "b": 280.86075, "coord_origin": "TOPLEFT"}}, {"id": 384, "text": "- lane use signs", "bbox": {"l": 479.97293, "t": 281.52759, "r": 491.00775000000004, "b": 283.02924, "coord_origin": "TOPLEFT"}}, {"id": 385, "text": "- turn control signs", "bbox": {"l": 479.97293, "t": 283.82556, "r": 493.32748, "b": 285.3272099999999, "coord_origin": "TOPLEFT"}}, {"id": 386, "text": "- parking signs", "bbox": {"l": 479.97293, "t": 286.1235, "r": 490.4915199999999, "b": 287.62518, "coord_origin": "TOPLEFT"}}, {"id": 387, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 288.42148, "r": 491.17004000000003, "b": 289.92316, "coord_origin": "TOPLEFT"}}, {"id": 388, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 290.05605999999995, "r": 484.77405000000005, "b": 292.09406, "coord_origin": "TOPLEFT"}}, {"id": 389, "text": "- warning signs", "bbox": {"l": 479.97293, "t": 292.76169000000004, "r": 490.83398, "b": 294.26334, "coord_origin": "TOPLEFT"}}, {"id": 390, "text": "- object markers", "bbox": {"l": 479.97293, "t": 295.05963, "r": 491.62692, "b": 296.56131, "coord_origin": "TOPLEFT"}}, {"id": 391, "text": "- construction ", "bbox": {"l": 479.97293, "t": 297.3576, "r": 490.37341, "b": 298.8592499999999, "coord_origin": "TOPLEFT"}}, {"id": 392, "text": "signs", "bbox": {"l": 481.21602999999993, "t": 298.99219, "r": 484.77405000000005, "b": 301.03015, "coord_origin": "TOPLEFT"}}, {"id": 393, "text": "- information and ", "bbox": {"l": 479.97293, "t": 301.69780999999995, "r": 492.93912, "b": 303.19946, "coord_origin": "TOPLEFT"}}, {"id": 394, "text": "destination signs", "bbox": {"l": 481.21602999999993, "t": 303.3324, "r": 493.00525, "b": 305.37036, "coord_origin": "TOPLEFT"}}, {"id": 395, "text": "- railway signs", "bbox": {"l": 479.97293, "t": 306.0379899999999, "r": 489.99047999999993, "b": 307.53967, "coord_origin": "TOPLEFT"}}, {"id": 396, "text": "\u2022", "bbox": {"l": 478.375, "t": 308.24789, "r": 479.1032400000001, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 397, "text": "Signals", "bbox": {"l": 479.83151, "t": 308.24789, "r": 484.92925999999994, "b": 310.28586, "coord_origin": "TOPLEFT"}}, {"id": 398, "text": "- lane control ", "bbox": {"l": 479.97293, "t": 310.95358, "r": 490.00091999999995, "b": 312.45526, "coord_origin": "TOPLEFT"}}, {"id": 399, "text": "signals", "bbox": {"l": 481.21602999999993, "t": 312.5881999999999, "r": 485.95331, "b": 314.62616, "coord_origin": "TOPLEFT"}}, {"id": 400, "text": "- traffic lights", "bbox": {"l": 479.97293, "t": 315.29379, "r": 489.29876999999993, "b": 316.79544, "coord_origin": "TOPLEFT"}}, {"id": 401, "text": "\u2022", "bbox": {"l": 478.375, "t": 317.50366, "r": 479.18129999999996, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 402, "text": "Road markings", "bbox": {"l": 479.98761, "t": 317.50366, "r": 490.46960000000007, "b": 319.5416599999999, "coord_origin": "TOPLEFT"}}, {"id": 403, "text": "- yellow lines", "bbox": {"l": 479.97293, "t": 320.20938, "r": 489.26166000000006, "b": 321.71103, "coord_origin": "TOPLEFT"}}, {"id": 404, "text": "- white lines", "bbox": {"l": 479.97293, "t": 322.50732, "r": 488.59189, "b": 324.009, "coord_origin": "TOPLEFT"}}, {"id": 405, "text": "- reserved lane ", "bbox": {"l": 479.97293, "t": 324.8053, "r": 491.17004000000003, "b": 326.30698, "coord_origin": "TOPLEFT"}}, {"id": 406, "text": "markings", "bbox": {"l": 481.21602999999993, "t": 326.43988, "r": 487.58978, "b": 328.47784, "coord_origin": "TOPLEFT"}}, {"id": 407, "text": "- other markings", "bbox": {"l": 479.97293, "t": 329.14551, "r": 491.75177, "b": 330.64716, "coord_origin": "TOPLEFT"}}, {"id": 408, "text": "in this chapter", "bbox": {"l": 478.15246999999994, "t": 265.07030999999995, "r": 493.75586, "b": 268.06872999999996, "coord_origin": "TOPLEFT"}}], "children": [{"id": 32, "label": "text", "bbox": {"l": 327.86951, "t": 351.78085, "r": 330.41248, "b": 353.95465, "coord_origin": 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"cells": [{"id": 80, "text": "1.", "bbox": {"l": 327.83005, "t": 336.40439, "r": 329.05914, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 36, "label": "text", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 81, "text": "The vertical tube can be used for", "bbox": {"l": 329.67368, "t": 336.40439, "r": 349.95349, "b": 337.92606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "instructional viewing or to photograph", "bbox": {"l": 329.11752, "t": 337.83588, "r": 353.57977, "b": 339.35751000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": " the image with a digital camera or a", "bbox": {"l": 327.77121, "t": 339.26736, "r": 352.4306, "b": 340.789, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": " micro TV unit", "bbox": {"l": 328.15176, "t": 340.69882, "r": 337.91086, "b": 342.22049, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 40, "label": "text", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "2.", "bbox": {"l": 327.8313, "t": 342.19043000000005, "r": 329.09155, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Loosen the retention screw, then rotate ", "bbox": {"l": 329.72168, "t": 342.19043000000005, "r": 354.9267, "b": 343.71207, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": " the adjustment ring to change the ", "bbox": {"l": 327.8313, "t": 343.62192, "r": 351.66949, "b": 345.14355, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": " length of the vertical tube.", "bbox": {"l": 328.21185, "t": 345.05338, "r": 346.33179, "b": 346.57504, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 327.83005, "t": 346.84680000000003, "r": 329.12726, "b": 348.36847, "coord_origin": "TOPLEFT"}, "confidence": 1.0, 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dif-", "bbox": {"l": 317.95499, "t": 540.08299, "r": 559.80579, "b": 548.55624, "coord_origin": "TOPLEFT"}}, {"id": 410, "text": "ferent document categories", "bbox": {"l": 317.95499, "t": 551.0419899999999, "r": 428.69907, "b": 559.51524, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1: Four examples of complex page layouts across different document categories"}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9208476543426514, "cells": [{"id": 411, "text": "KEYWORDS", "bbox": {"l": 317.95499, "t": 592.46591, "r": 379.8205, "b": 602.7750100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KEYWORDS"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.95499, "t": 607.66756, "r": 559.18597, "b": 627.00117, "coord_origin": "TOPLEFT"}, 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{"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 650.11996, "r": 559.5495, "b": 707.377029, "coord_origin": "TOPLEFT"}, "confidence": 0.9455165863037109, "cells": [{"id": 415, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter", "bbox": {"l": 317.95499, "t": 650.11996, "r": 558.35266, "b": 657.56404, "coord_origin": "TOPLEFT"}}, {"id": 416, "text": "Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document-", "bbox": {"l": 317.95499, "t": 660.08296, "r": 559.5495, "b": 667.52703, "coord_origin": "TOPLEFT"}}, {"id": 417, "text": "Layout Analysis. In", "bbox": {"l": 317.95499, "t": 670.04497, "r": 383.30807, "b": 677.48904, "coord_origin": "TOPLEFT"}}, {"id": 418, "text": "Proceedings of the 28th ACM SIGKDD Conference on", "bbox": {"l": 385.798, "t": 670.08482, "r": 558.20032, "b": 677.49701, "coord_origin": "TOPLEFT"}}, {"id": 419, "text": "Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Wash-", "bbox": {"l": 317.95499, "t": 680.04781, "r": 559.00092, "b": 687.46001, "coord_origin": "TOPLEFT"}}, {"id": 420, "text": "ington, DC, USA.", "bbox": {"l": 317.95499, "t": 690.01081, "r": 370.11481, "b": 697.423004, "coord_origin": "TOPLEFT"}}, {"id": 421, "text": "ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/", "bbox": {"l": 371.82999, "t": 689.97096, "r": 558.71655, "b": 697.415031, "coord_origin": "TOPLEFT"}}, {"id": 422, "text": "3534678.3539043", "bbox": {"l": 317.95499, "t": 699.932953, "r": 371.59375, "b": 707.377029, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8537883758544922, "cells": [{"id": 423, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "bbox": {"l": 18.34021, "t": 218.35999000000004, "r": 36.339794, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"id": 2, "text": "1", "bbox": {"l": 53.79800000000001, "t": 85.85986000000003, "r": 59.427395, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "INTRODUCTION", "bbox": {"l": 70.379532, "t": 85.85986000000003, "r": 156.52899, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.528999, "t": 110.98352, "r": 303.01697, "b": 228.94714, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"id": 4, "text": "Despite the substantial improvements achieved with machine-learning", "bbox": {"l": 53.79800000000001, "t": 110.98352, "r": 303.01697, "b": 119.35815000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "(ML) approaches and deep neural networks in recent years, docu-", "bbox": {"l": 53.528999, "t": 121.94256999999993, "r": 295.55695, "b": 130.31719999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ment conversion remains a challenging problem, as demonstrated", "bbox": {"l": 53.79800000000001, "t": 132.90155000000004, "r": 294.04642, "b": 141.27617999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "by the numerous public competitions held on this topic [1-4]. The", "bbox": {"l": 53.79800000000001, "t": 143.85956, "r": 294.04733, "b": 152.23419, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "challenge originates from the huge variability in PDF documents", "bbox": {"l": 53.79800000000001, "t": 154.81853999999998, "r": 294.04349, "b": 163.19317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "regarding layout, language and formats (scanned, programmatic", "bbox": {"l": 53.79800000000001, "t": 165.77752999999996, "r": 294.04718, "b": 174.15215999999998, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "or a combination of both). Engineering a single ML model that can", "bbox": {"l": 53.79800000000001, "t": 176.73650999999995, "r": 294.04919, "b": 185.11114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "be applied on all types of documents and provides high-quality", "bbox": {"l": 53.79800000000001, "t": 187.69556, "r": 294.27573, "b": 196.07019000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "layout segmentation remains to this day extremely challenging [5].", "bbox": {"l": 53.79800000000001, "t": 198.65454, "r": 295.42569, "b": 207.02917000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "To highlight the variability in document layouts, we show a few", "bbox": {"l": 53.528999, "t": 209.61352999999997, "r": 294.37256, "b": 217.98816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "example documents from the DocLayNet dataset in Figure 1.", "bbox": {"l": 53.79800000000001, "t": 220.57250999999997, "r": 275.48334, "b": 228.94714, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.528999, "t": 231.53156, "r": 295.56412, "b": 502.91916, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"id": 15, "text": "A key problem in the process of document conversion is to under-", "bbox": {"l": 63.76100199999999, "t": 231.53156, "r": 295.564, "b": 239.90619000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "stand the structure of a single document page, i.e. which segments", "bbox": {"l": 53.79800000000001, "t": 242.49054, "r": 294.04868, "b": 250.86517000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "of text should be grouped together in a unit. To train models for this", "bbox": {"l": 53.79800000000001, "t": 253.44854999999995, "r": 294.04532, "b": 261.82318, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "task, there are currently two large datasets available to the com-", "bbox": {"l": 53.79800000000001, "t": 264.40752999999995, "r": 295.55618, "b": 272.78216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "bbox": {"l": 53.79800000000001, "t": 275.36658, "r": 294.04059, "b": 283.74118, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "bbox": {"l": 53.79800000000001, "t": 286.32552999999996, "r": 295.55783, "b": 294.70016, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "plementation of layout detection and segmentation models due to", "bbox": {"l": 53.79800000000001, "t": 297.28455, "r": 294.04538, "b": 305.65918000000005, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "bbox": {"l": 53.79800000000001, "t": 308.24353, "r": 294.043, "b": 316.61816, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "achieved by leveraging an automation approach. The benefit of au-", "bbox": {"l": 53.79800000000001, "t": 319.20255, "r": 295.56412, "b": 327.57718, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tomated ground-truth generation is obvious: one can generate large", "bbox": {"l": 53.79800000000001, "t": 330.16153, "r": 294.04532, "b": 338.53616, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ground-truth datasets at virtually no cost. However, the automation", "bbox": {"l": 53.79800000000001, "t": 341.12054, "r": 294.04538, "b": 349.49518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "introduces a constraint on the variability in the dataset, because", "bbox": {"l": 53.79800000000001, "t": 352.07953, "r": 294.04712, "b": 360.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "corresponding structured source data must be available. PubLayNet", "bbox": {"l": 53.79800000000001, "t": 363.03853999999995, "r": 294.04538, "b": 371.41318, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and DocBank were both generated from scientific document repos-", "bbox": {"l": 53.79800000000001, "t": 373.99655, "r": 295.55643, "b": 382.37119, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "itories (PubMed and arXiv), which provide XML or L", "bbox": {"l": 53.79800000000001, "t": 384.95554, "r": 246.75909, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "A", "bbox": {"l": 243.53101000000004, "t": 385.03183000000007, "r": 248.58553000000003, "b": 391.82455, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "T", "bbox": {"l": 247.24099999999999, "t": 384.95554, "r": 252.58859, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "E", "bbox": {"l": 251.09398999999996, "t": 386.87954999999994, "r": 256.08829, "b": 395.25418, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 254.967, "t": 384.95554, "r": 261.33725, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "sources.", "bbox": {"l": 263.75021, "t": 384.95554, "r": 295.42773, "b": 393.33017, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Those scientific documents present a limited variability in their", "bbox": {"l": 53.528999, "t": 395.91455, "r": 294.21713, "b": 404.28918, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "layouts, because they are typeset in uniform templates provided by", "bbox": {"l": 53.79800000000001, "t": 406.87354, "r": 294.27386, "b": 415.24817, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "the publishers. Obviously, documents such as technical manuals,", "bbox": {"l": 53.79800000000001, "t": 417.8325500000001, "r": 295.03488, "b": 426.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "annual company reports, legal text, government tenders, etc. have", "bbox": {"l": 53.79800000000001, "t": 428.79153, "r": 294.04691, "b": 437.16617, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "very different and partially unique layouts. As a consequence, the", "bbox": {"l": 53.57400100000001, "t": 439.75055, "r": 294.04865, "b": 448.12518, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "layout predictions obtained from models trained on PubLayNet or", "bbox": {"l": 53.79800000000001, "t": 450.7095299999999, "r": 294.21643, "b": 459.08417, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "DocBank is very reasonable when applied on scientific documents.", "bbox": {"l": 53.79800000000001, "t": 461.66855, "r": 295.42181, "b": 470.04318, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "However, for more", "bbox": {"l": 53.79800000000001, "t": 472.62753, "r": 125.52795, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "artistic", "bbox": {"l": 128.608, "t": 472.67239, "r": 153.7679, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "or", "bbox": {"l": 157.15199, "t": 472.62753, "r": 165.16365, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "free-style", "bbox": {"l": 168.248, "t": 472.67239, "r": 201.49272, "b": 481.01114, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "layouts, we see sub-par", "bbox": {"l": 204.78799, "t": 472.62753, "r": 294.21494, "b": 481.00217, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "prediction quality from these models, which we demonstrate in", "bbox": {"l": 53.79800000000001, "t": 483.58554, "r": 294.04715, "b": 491.96017, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Section 5.", "bbox": {"l": 53.79800000000001, "t": 494.54453, "r": 89.080788, "b": 502.91916, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.591999, "t": 505.50354, "r": 295.56396, "b": 579.63217, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"id": 49, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "bbox": {"l": 63.76100199999999, "t": 505.50354, "r": 295.56396, "b": 513.87817, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "by-page layout annotation ground-truth using bounding-boxes for", "bbox": {"l": 53.79800000000001, "t": 516.46252, "r": 294.21271, "b": 524.83716, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "11", "bbox": {"l": 53.591999, "t": 527.42154, "r": 61.92275599999999, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "distinct class labels on 80863 unique document pages, of which", "bbox": {"l": 64.162201, "t": 527.42154, "r": 294.04626, "b": 535.7961700000001, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "bbox": {"l": 53.79800000000001, "t": 538.38055, "r": 294.21228, "b": 546.75517, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "bbox": {"l": 53.79800000000001, "t": 549.33955, "r": 294.04709, "b": 557.71417, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "available to the public", "bbox": {"l": 53.79800000000001, "t": 560.29855, "r": 134.28951, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1", "bbox": {"l": 134.29201, "t": 558.23083, "r": 137.67381, "b": 565.0235299999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "in order to stimulate the document-layout", "bbox": {"l": 140.418, "t": 560.29855, "r": 294.047, "b": 568.67317, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "analysis community. It distinguishes itself in the following aspects:", "bbox": {"l": 53.79800000000001, "t": 571.25755, "r": 295.10538, "b": 579.63217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.58156, "r": 295.56165, "b": 614.87418, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"id": 59, "text": "(1)", "bbox": {"l": 64.708, "t": 584.58156, "r": 74.221352, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Human Annotation", "bbox": {"l": 78.207001, "t": 584.62639, "r": 146.39589, "b": 592.96515, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ": In contrast to PubLayNet and DocBank,", "bbox": {"l": 146.41701, "t": 584.58156, "r": 295.03036, "b": 592.95618, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "we relied on human annotation instead of automation ap-", "bbox": {"l": 77.875, "t": 595.54056, "r": 295.56165, "b": 603.91518, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "proaches to generate the data set.", "bbox": {"l": 78.207001, "t": 606.49956, "r": 200.6432, "b": 614.87418, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.45856, "r": 294.26254, "b": 636.79117, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"id": 64, "text": "(2)", "bbox": {"l": 64.708, "t": 617.45856, "r": 74.221352, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Large Layout Variability", "bbox": {"l": 78.207001, "t": 617.50339, "r": 167.91745, "b": 625.84215, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": ": We include diverse and complex", "bbox": {"l": 168.33501, "t": 617.45856, "r": 294.26254, "b": 625.8331800000001, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "layouts from a large variety of public sources.", "bbox": {"l": 78.207001, "t": 628.41655, "r": 245.45726000000002, "b": 636.79117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.37555, "r": 294.68381, "b": 669.66817, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"id": 68, "text": "(3)", "bbox": {"l": 64.708, "t": 639.37555, "r": 74.221352, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Detailed Label Set", "bbox": {"l": 78.207001, "t": 639.42038, "r": 143.51663, "b": 647.75914, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ": We define 11 class labels to distinguish", "bbox": {"l": 144.02, "t": 639.37555, "r": 294.04648, "b": 647.75017, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "layout features in high detail. PubLayNet provides 5 labels;", "bbox": {"l": 78.207001, "t": 650.33455, "r": 294.68381, "b": 658.70917, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "DocBank provides 13, although not a superset of ours.", "bbox": {"l": 78.207001, "t": 661.29355, "r": 276.33752, "b": 669.66817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.25256, "r": 295.56439, "b": 691.58617, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"id": 73, "text": "(4)", "bbox": {"l": 64.708, "t": 672.25256, "r": 74.221352, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Redundant Annotations", "bbox": {"l": 78.207001, "t": 672.29739, "r": 163.78357, "b": 680.63614, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ": A fraction of the pages in the Do-", "bbox": {"l": 163.994, "t": 672.25256, "r": 295.56439, "b": 680.62717, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "cLayNet data set carry more than one human annotation.", "bbox": {"l": 78.207001, "t": 683.21156, "r": 295.42719, "b": 691.58617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"id": 77, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet", "bbox": {"l": 53.672001, "t": 702.226364, "r": 216.02750000000003, "b": 708.739891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{1}$https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"id": 78, "text": "This enables experimentation with annotation uncertainty", "bbox": {"l": 342.095, "t": 87.36352999999997, "r": 558.43201, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "and quality control analysis.", "bbox": {"l": 342.36401, "t": 98.32250999999997, "r": 445.83629999999994, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 559.72101, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"id": 80, "text": "(5)", "bbox": {"l": 328.86502, "t": 109.28148999999996, "r": 338.37836, "b": 117.65612999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Pre-defined Train-, Test- & Validation-set", "bbox": {"l": 342.36401, "t": 109.32641999999998, "r": 487.25296, "b": 117.66516000000001, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ": Like DocBank, we", "bbox": {"l": 487.75900000000007, "t": 109.28156000000001, "r": 558.20294, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "bbox": {"l": 342.36401, "t": 120.24054000000001, "r": 559.72101, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "tional representation of the class-labels. Further, we prevent", "bbox": {"l": 342.36401, "t": 131.19854999999995, "r": 558.20117, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "leakage of unique layouts across sets, which has a large effect", "bbox": {"l": 342.36401, "t": 142.15752999999995, "r": 558.20087, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on model accuracy scores.", "bbox": {"l": 342.36401, "t": 153.11652000000004, "r": 438.0624399999999, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.62299, "t": 167.97551999999996, "r": 559.19031, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"id": 87, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "bbox": {"l": 327.918, "t": 167.97551999999996, "r": 559.19031, "b": 176.35015999999996, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "we will elaborate on how we designed and executed this large-scale", "bbox": {"l": 317.62299, "t": 178.93451000000005, "r": 558.20422, "b": 187.30913999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "human annotation campaign. We will also share key insights and", "bbox": {"l": 317.95499, "t": 189.89355, "r": 558.19763, "b": 198.26819, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "lessons learned that might prove helpful for other parties planning", "bbox": {"l": 317.95499, "t": 200.85253999999998, "r": 558.20612, "b": 209.22717, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "to set up annotation campaigns.", "bbox": {"l": 317.95499, "t": 211.81151999999997, "r": 434.94861, "b": 220.18615999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.73099, "t": 222.77057000000002, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"id": 92, "text": "In Section 5, we will present baseline accuracy numbers for a", "bbox": {"l": 327.918, "t": 222.77057000000002, "r": 558.19836, "b": 231.14520000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "bbox": {"l": 317.73099, "t": 233.72955000000002, "r": 558.1991, "b": 242.10419000000002, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "bbox": {"l": 317.95499, "t": 244.68854, "r": 558.20416, "b": 253.06317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "model performance is impacted by varying the DocLayNet dataset", "bbox": {"l": 317.95499, "t": 255.64752, "r": 558.20563, "b": 264.02216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "size, reducing the label set and modifying the train/test-split. Last", "bbox": {"l": 317.95499, "t": 266.60553000000004, "r": 558.19861, "b": 274.98015999999996, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "but not least, we compare the performance of models trained on", "bbox": {"l": 317.95499, "t": 277.56458, "r": 558.20416, "b": 285.93918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "bbox": {"l": 317.95499, "t": 288.52353, "r": 558.20239, "b": 296.89816, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "trained on DocLayNet provides overall more robust layout recovery.", "bbox": {"l": 317.95499, "t": 299.48254, "r": 559.58197, "b": 307.85718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.95499, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"id": 100, "text": "2", "bbox": {"l": 317.95499, "t": 321.20889, "r": 323.10388, "b": 331.51797, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "RELATED WORK", "bbox": {"l": 333.12115, "t": 321.20889, "r": 421.74411, "b": 331.51797, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71613, "b": 464.29617, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"id": 102, "text": "While early approaches in document-layout analysis used rule-", "bbox": {"l": 317.52499, "t": 346.3325500000001, "r": 559.71301, "b": 354.70717999999994, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "based algorithms and heuristics [8], the problem is lately addressed", "bbox": {"l": 317.95499, "t": 357.29153, "r": 558.20276, "b": 365.66617, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "with deep learning methods. The most common approach is to lever-", "bbox": {"l": 317.62299, "t": 368.25055, "r": 559.71564, "b": 376.62518, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "age object detection models [9-15]. In the last decade, the accuracy", "bbox": {"l": 317.95499, "t": 379.2095299999999, "r": 558.43365, "b": 387.58417, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "and speed of these models has increased dramatically. Furthermore,", "bbox": {"l": 317.95499, "t": 390.16855000000004, "r": 559.18658, "b": 398.54318, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "most state-of-the-art object detection methods can be trained and", "bbox": {"l": 317.95499, "t": 401.12753, "r": 558.20502, "b": 409.50217, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "applied with very little work, thanks to a standardisation effort", "bbox": {"l": 317.95499, "t": 412.08655, "r": 558.20422, "b": 420.46118, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "of the ground-truth data format [16] and common deep-learning", "bbox": {"l": 317.95499, "t": 423.04553, "r": 558.20477, "b": 431.4201699999999, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "bbox": {"l": 317.95499, "t": 434.00354, "r": 558.19952, "b": 442.37817, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "DocBank provide their data in the commonly accepted COCO for-", "bbox": {"l": 317.95499, "t": 444.96252, "r": 559.71613, "b": 453.33716, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "mat [16].", "bbox": {"l": 317.95499, "t": 455.92154, "r": 350.90652, "b": 464.29617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 466.88052, "r": 559.18646, "b": 551.96817, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"id": 113, "text": "Lately, new types of ML models for document-layout analysis", "bbox": {"l": 327.918, "t": 466.88052, "r": 558.19824, "b": 475.25516, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "have emerged in the community [18-21]. These models do not", "bbox": {"l": 317.95499, "t": 477.83954, "r": 558.20551, "b": 486.21417, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "approach the problem of layout analysis purely based on an image", "bbox": {"l": 317.95499, "t": 488.79855, "r": 558.20575, "b": 497.17319, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "representation of the page, as computer vision methods do. Instead,", "bbox": {"l": 317.95499, "t": 499.75754, "r": 559.18646, "b": 508.13217, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "they combine the text tokens and image representation of a page", "bbox": {"l": 317.95499, "t": 510.71655, "r": 558.2002, "b": 519.09119, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "in order to obtain a segmentation. While the reported accuracies", "bbox": {"l": 317.95499, "t": 521.67554, "r": 558.20618, "b": 530.05017, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "appear to be promising, a broadly accepted data format which links", "bbox": {"l": 317.95499, "t": 532.63455, "r": 558.20239, "b": 541.00917, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "geometric and textual features has yet to establish.", "bbox": {"l": 317.95499, "t": 543.59355, "r": 503.32648, "b": 551.96817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.95499, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"id": 121, "text": "3", "bbox": {"l": 317.95499, "t": 565.3199, "r": 322.97391, "b": 575.629, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "THE DOCLAYNET DATASET", "bbox": {"l": 332.73831, "t": 565.3199, "r": 477.45688, "b": 575.629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 559.7132, "b": 675.53017, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"id": 123, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "bbox": {"l": 317.95499, "t": 590.4435599999999, "r": 558.20233, "b": 598.81818, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "instances of human annotations, and 1591 carry three. This amounts", "bbox": {"l": 317.95499, "t": 601.40256, "r": 558.20239, "b": 609.77718, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "to 91104 total annotation instances. The annotations provide lay-", "bbox": {"l": 317.95499, "t": 612.36055, "r": 559.7132, "b": 620.73517, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "out information in the shape of labeled, rectangular bounding-", "bbox": {"l": 317.95499, "t": 623.3195499999999, "r": 559.71313, "b": 631.69417, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "boxes. We define 11 distinct labels for layout features, namely", "bbox": {"l": 317.95499, "t": 634.27855, "r": 539.92047, "b": 642.65317, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "Cap-", "bbox": {"l": 542.15802, "t": 634.32338, "r": 559.09888, "b": 642.66214, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "tion", "bbox": {"l": 317.95499, "t": 645.28238, "r": 331.86273, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": ",", "bbox": {"l": 331.86301, "t": 645.23755, "r": 333.83957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "Footnote", "bbox": {"l": 336.064, "t": 645.28238, "r": 366.05368, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": ",", "bbox": {"l": 366.05301, "t": 645.23755, "r": 368.02957, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "Formula", "bbox": {"l": 370.254, "t": 645.28238, "r": 400.05502, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": ",", "bbox": {"l": 400.05499, "t": 645.23755, "r": 402.03156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "List-item", "bbox": {"l": 404.25601, "t": 645.28238, "r": 436.19531, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": ",", "bbox": {"l": 436.19501, "t": 645.23755, "r": 438.17157, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "Page-footer", "bbox": {"l": 440.396, "t": 645.28238, "r": 480.60988999999995, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": ",", "bbox": {"l": 480.60999, "t": 645.23755, "r": 482.58655000000005, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "Page-header", "bbox": {"l": 484.811, "t": 645.28238, "r": 528.37604, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": ",", "bbox": {"l": 528.375, "t": 645.23755, "r": 530.35156, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "Picture", "bbox": {"l": 532.57599, "t": 645.28238, "r": 557.211, "b": 653.62114, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": ",", "bbox": {"l": 557.211, "t": 645.23755, "r": 559.18756, "b": 653.61217, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "Section-header", "bbox": {"l": 317.95499, "t": 656.2413799999999, "r": 368.78821, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": ",", "bbox": {"l": 368.789, "t": 656.19655, "r": 370.72217, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "Table", "bbox": {"l": 372.89999, "t": 656.2413799999999, "r": 391.57254, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": ",", "bbox": {"l": 391.573, "t": 656.19655, "r": 393.50616, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "Text", "bbox": {"l": 395.68399, "t": 656.2413799999999, "r": 410.23538, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": ", and", "bbox": {"l": 410.23099, "t": 656.19655, "r": 427.5679, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "Title", "bbox": {"l": 429.74399, "t": 656.2413799999999, "r": 445.50800000000004, "b": 664.58013, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": ". Our reasoning for picking this", "bbox": {"l": 445.50800000000004, "t": 656.19655, "r": 558.20227, "b": 664.5711699999999, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "particular label set is detailed in Section 4.", "bbox": {"l": 317.95499, "t": 667.15556, "r": 472.22198, "b": 675.53017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 678.11456, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"id": 152, "text": "In addition to open intellectual property constraints for the", "bbox": {"l": 327.918, "t": 678.11456, "r": 558.19843, "b": 686.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "source documents, we required that the documents in DocLayNet", "bbox": {"l": 317.95499, "t": 689.07355, "r": 558.19867, "b": 697.448174, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "bbox": {"l": 317.95499, "t": 700.032555, "r": 558.2041, "b": 708.4071730000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 7, "text": "32%", "bbox": {"l": 136.24422, "t": 130.24408000000005, "r": 151.50592, "b": 138.77112, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "Tenders", "bbox": {"l": 93.973373, "t": 187.65765, "r": 121.11515, "b": 196.18469000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "6%", "bbox": {"l": 139.6235, "t": 170.22748, "r": 150.64458, "b": 178.75451999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Laws", "bbox": {"l": 139.88339, "t": 212.50036999999998, "r": 157.68491, "b": 221.02739999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "16%", "bbox": {"l": 157.43983, "t": 183.77808000000005, "r": 172.70154, "b": 192.30511, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Manuals", "bbox": {"l": 225.47252, "t": 189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 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189.29656999999997, "r": 254.29510000000002, "b": 197.82361000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "21%", "bbox": {"l": 194.40683, "t": 171.12145999999996, "r": 209.66853, "b": 179.6485, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 255.54724, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"id": 14, "text": "Figure 2: Distribution of DocLayNet pages across document", "bbox": {"l": 53.79800000000001, "t": 236.11499000000003, "r": 294.04373, "b": 244.58826, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "categories.", "bbox": {"l": 53.79800000000001, "t": 247.07397000000003, "r": 96.756027, "b": 255.54724, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "17%", "bbox": {"l": 184.40349, "t": 118.68206999999995, "r": 199.66519, "b": 127.20911000000001, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Financial", "bbox": {"l": 88.288223, "t": 114.35473999999988, "r": 118.80401, "b": 122.88176999999985, 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"coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 2, "text": "Patents", "bbox": {"l": 237.11293, "t": 133.08716000000004, "r": 262.97623, "b": 141.61419999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "8%", "bbox": {"l": 202.87892, "t": 140.46178999999995, "r": 213.89999, "b": 148.98883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 101.68469000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 4, "text": "Scientific", "bbox": {"l": 207.13306, "t": 93.1576500000001, "r": 237.64882999999998, "b": 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281.8035300000001, "r": 294.27383, "b": 366.89017, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"id": 16, "text": "to a minimum, since they introduce difficulties in annotation (see", "bbox": {"l": 53.79800000000001, "t": 281.8035300000001, "r": 294.04605, "b": 290.17815999999993, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Section 4). As a second condition, we focussed on medium to large", "bbox": {"l": 53.79800000000001, "t": 292.76254, "r": 294.04868, "b": 301.13718, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "documents (", "bbox": {"l": 53.79800000000001, "t": 303.72153, "r": 98.881348, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ">", "bbox": {"l": 99.070999, "t": 306.07971, "r": 104.77363, "b": 310.08768, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "10", "bbox": {"l": 107.46399999999998, "t": 303.72153, "r": 115.83373999999999, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "pages) with technical content, dense in complex", "bbox": {"l": 118.08366, "t": 303.72153, "r": 294.26233, "b": 312.09616, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "tables, figures, plots and captions. Such documents carry a lot of", "bbox": {"l": 53.79800000000001, "t": 314.68054, "r": 294.04715, "b": 323.05518, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "information value, but are often hard to analyse with high accuracy", "bbox": {"l": 53.79800000000001, "t": 325.63855, "r": 294.27383, "b": 334.01318, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "due to their challenging layouts. Counterexamples of documents", "bbox": {"l": 53.79800000000001, "t": 336.59753, "r": 294.0416, "b": 344.97217, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "not included in the dataset are receipts, invoices, hand-written", "bbox": {"l": 53.79800000000001, "t": 347.5565500000001, "r": 294.04712, "b": 355.9311799999999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "documents or photographs showing \u201ctext in the wild\".", "bbox": {"l": 53.79800000000001, "t": 358.51553, "r": 251.73131, "b": 366.89017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing \u201ctext in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.57400100000001, "t": 369.47455, "r": 295.56046, "b": 509.35617, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"id": 27, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "bbox": {"l": 63.76100199999999, "t": 369.47455, "r": 295.55945, "b": 377.84918, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "gories, namely", "bbox": {"l": 53.79800000000001, "t": 380.43353, "r": 105.90533, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Financial Reports", "bbox": {"l": 107.754, "t": 380.47838999999993, "r": 167.4973, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": ",", "bbox": {"l": 167.496, "t": 380.43353, "r": 169.42915, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Manuals", "bbox": {"l": 171.28101, "t": 380.47838999999993, "r": 201.45581, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": ",", "bbox": {"l": 201.455, "t": 380.43353, "r": 203.38815, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Scientific Articles", "bbox": {"l": 205.24001, "t": 380.47838999999993, "r": 264.55273, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": ",", "bbox": {"l": 264.54901, "t": 380.43353, "r": 266.48218, "b": 388.80816999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Laws &", "bbox": {"l": 268.33401, "t": 380.47838999999993, "r": 294.36133, "b": 388.81714, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Regulations", "bbox": {"l": 53.79800000000001, "t": 391.43741000000006, "r": 94.899666, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": ",", "bbox": {"l": 94.900002, "t": 391.39255, "r": 96.862747, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "Patents", "bbox": {"l": 99.109001, "t": 391.43741000000006, "r": 124.72282, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "and", "bbox": {"l": 127.17899999999999, "t": 391.39255, "r": 140.60596, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Government Tenders", "bbox": {"l": 142.853, "t": 391.43741000000006, "r": 215.15340000000003, "b": 399.77615, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": ". Each document cate-", "bbox": {"l": 215.15601000000004, "t": 391.39255, "r": 295.55716, "b": 399.76718, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "gory was sourced from various repositories. For example, Financial", "bbox": {"l": 53.79800000000001, "t": 402.3515300000001, "r": 294.04535, "b": 410.72617, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Reports contain both", "bbox": {"l": 53.79800000000001, "t": 413.31055, "r": 132.19516, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "free-style", "bbox": {"l": 134.528, "t": 413.35541, "r": 167.77272, "b": 421.69415, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "format annual reports", "bbox": {"l": 170.314, "t": 413.31055, "r": 252.36031, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2", "bbox": {"l": 252.356, "t": 411.24283, "r": 255.73781, "b": 418.03555, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "which ex-", "bbox": {"l": 258.56601, "t": 413.31055, "r": 295.56046, "b": 421.68518000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "pose company-specific, artistic layouts as well as the more formal", "bbox": {"l": 53.79800000000001, "t": 424.26953, "r": 294.04376, "b": 432.6441699999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "SEC filings. The two largest categories (", "bbox": {"l": 53.79800000000001, "t": 435.22754000000003, "r": 197.59023, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Financial Reports", "bbox": {"l": 197.591, "t": 435.27240000000006, "r": 258.02774, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "and", "bbox": {"l": 260.48901, "t": 435.22754000000003, "r": 273.78104, "b": 443.60217, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Man-", "bbox": {"l": 276.03201, "t": 435.27240000000006, "r": 294.94113, "b": 443.61115, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "uals", "bbox": {"l": 53.79800000000001, "t": 446.23138, "r": 68.085777, "b": 454.57013, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ") contain a large amount of free-style layouts in order to obtain", "bbox": {"l": 68.296997, "t": 446.18652, "r": 294.04565, "b": 454.56116, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "maximum variability. In the other four categories, we boosted the", "bbox": {"l": 53.79800000000001, "t": 457.14554, "r": 294.04163, "b": 465.52017, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "variability by mixing documents from independent providers, such", "bbox": {"l": 53.57400100000001, "t": 468.10455, "r": 294.04889, "b": 476.47919, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "as different government websites or publishers. In Figure 2, we", "bbox": {"l": 53.79800000000001, "t": 479.06354, "r": 294.04715, "b": 487.43817, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "show the document categories contained in DocLayNet with their", "bbox": {"l": 53.79800000000001, "t": 490.02255, "r": 294.21643, "b": 498.39719, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "respective sizes.", "bbox": {"l": 53.79800000000001, "t": 500.98154, "r": 112.2948, "b": 509.35617, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 511.94055, "r": 295.56155, "b": 607.98618, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"id": 60, "text": "We did not control the document selection with regard to lan-", "bbox": {"l": 63.76100199999999, "t": 511.94055, "r": 295.55954, "b": 520.31519, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "guage. The vast majority of documents contained in DocLayNet", "bbox": {"l": 53.79800000000001, "t": 522.89954, "r": 294.04718, "b": 531.27417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "(close to 95%) are published in English language. However, Do-", "bbox": {"l": 53.528999, "t": 533.8585499999999, "r": 295.56155, "b": 542.23317, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "cLayNet also contains a number of documents in other languages", "bbox": {"l": 53.79800000000001, "t": 544.81755, "r": 294.04144, "b": 553.19217, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "bbox": {"l": 53.79800000000001, "t": 555.77556, "r": 294.04709, "b": 564.15018, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "the document language has negligible impact on the performance", "bbox": {"l": 53.79800000000001, "t": 566.73456, "r": 294.04163, "b": 575.10918, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "of computer vision methods such as object detection and segmenta-", "bbox": {"l": 53.79800000000001, "t": 577.6935599999999, "r": 295.5567, "b": 586.06818, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "tion models, it might prove challenging for layout analysis methods", "bbox": {"l": 53.79800000000001, "t": 588.65256, "r": 294.04541, "b": 597.02718, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "which exploit textual features.", "bbox": {"l": 53.466999, "t": 599.61156, "r": 164.39928, "b": 607.98618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 610.57056, "r": 295.56396, "b": 684.69817, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"id": 69, "text": "To ensure that future benchmarks in the document-layout analy-", "bbox": {"l": 63.76100199999999, "t": 610.57056, "r": 295.56396, "b": 618.9451799999999, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "sis community can be easily compared, we have split up DocLayNet", "bbox": {"l": 53.79800000000001, "t": 621.52956, "r": 294.04532, "b": 629.90417, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "bbox": {"l": 53.79800000000001, "t": 632.48856, "r": 294.04538, "b": 640.86317, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "avoid spurious variations in the evaluation scores due to random", "bbox": {"l": 53.79800000000001, "t": 643.4475600000001, "r": 294.04315, "b": 651.82217, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "splitting in train-, test- and validation-sets. We also ensured that", "bbox": {"l": 53.79800000000001, "t": 654.40656, "r": 294.04712, "b": 662.78117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "less frequent labels are represented in train and test sets in equal", "bbox": {"l": 53.79800000000001, "t": 665.36456, "r": 294.04333, "b": 673.7391700000001, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "proportions.", "bbox": {"l": 53.79800000000001, "t": 676.32355, "r": 98.916931, "b": 684.69817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"id": 76, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/", "bbox": {"l": 53.79800000000001, "t": 701.6563639999999, "r": 195.78998, "b": 708.169891, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{2}$e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 87.36352999999997, "r": 559.19183, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"id": 77, "text": "Table 1 shows the overall frequency and distribution of the labels", "bbox": {"l": 327.918, "t": 87.36352999999997, "r": 558.20093, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "among the different sets. Importantly, we ensure that subsets are", "bbox": {"l": 317.95499, "t": 98.32250999999997, "r": 558.20013, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "only split on full-document boundaries. This avoids that pages of", "bbox": {"l": 317.95499, "t": 109.28156000000001, "r": 558.20056, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "the same document are spread over train, test and validation set,", "bbox": {"l": 317.95499, "t": 120.24054000000001, "r": 559.19183, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "which can give an undesired evaluation advantage to models and", "bbox": {"l": 317.62299, "t": 131.19854999999995, "r": 558.20349, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "lead to overestimation of their prediction accuracy. We will show", "bbox": {"l": 317.95499, "t": 142.15752999999995, "r": 558.52936, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "the impact of this decision in Section 5.", "bbox": {"l": 317.95499, "t": 153.11652000000004, "r": 461.6416, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.95499, "t": 164.07556, "r": 558.43811, "b": 271.0802, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"id": 84, "text": "In order to accommodate the different types of models currently", "bbox": {"l": 327.918, "t": 164.07556, "r": 558.43811, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "in use by the community, we provide DocLayNet in an", "bbox": {"l": 317.95499, "t": 175.03454999999997, "r": 516.8219, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "augmented", "bbox": {"l": 519.07501, "t": 175.07941000000005, "r": 558.20135, "b": 183.41814999999997, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "COCO format [16]. This entails the standard COCO ground-truth", "bbox": {"l": 317.95499, "t": 185.99352999999996, "r": 558.20325, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "file (in JSON format) with the associated page images (in PNG", "bbox": {"l": 317.95499, "t": 196.95250999999996, "r": 558.20404, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "format, 1025", "bbox": {"l": 317.95499, "t": 207.91156, "r": 364.28769, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "\u00d7", "bbox": {"l": 364.28699, "t": 207.85779000000002, "r": 369.98962, "b": 215.55993999999998, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "1025 pixels). Furthermore, custom fields have been", "bbox": {"l": 369.98999, "t": 207.91156, "r": 558.20526, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "added to each COCO record to specify document category, original", "bbox": {"l": 317.95499, "t": 218.87054, "r": 558.20251, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "document filename and page number. In addition, we also provide", "bbox": {"l": 317.95499, "t": 229.82952999999998, "r": 558.203, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "the original PDF pages, as well as sidecar files containing parsed", "bbox": {"l": 317.95499, "t": 240.78754000000004, "r": 558.20404, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "bbox": {"l": 317.95499, "t": 251.74652000000003, "r": 558.20227, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "linked to the primary page images by their matching filenames.", "bbox": {"l": 317.95499, "t": 262.70556999999997, "r": 550.36414, "b": 271.0802, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.94199, "t": 273.66454999999996, "r": 559.72156, "b": 588.88918, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"id": 97, "text": "Despite being cost-intense and far less scalable than automation,", "bbox": {"l": 327.918, "t": 273.66454999999996, "r": 559.18488, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "human annotation has several benefits over automated ground-", "bbox": {"l": 317.95499, "t": 284.62354, "r": 559.7132, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "truth generation. The first and most obvious reason to leverage", "bbox": {"l": 317.95499, "t": 295.5825500000001, "r": 558.20416, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "human annotations is the freedom to annotate any type of doc-", "bbox": {"l": 317.95499, "t": 306.54153, "r": 559.71326, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "ument without requiring a programmatic source. For most PDF", "bbox": {"l": 317.95499, "t": 317.50055, "r": 558.41443, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "documents, the original source document is not available. The lat-", "bbox": {"l": 317.95499, "t": 328.4595299999999, "r": 559.71545, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "ter is not a hard constraint with human annotation, but it is for", "bbox": {"l": 317.95499, "t": 339.41855000000004, "r": 558.36865, "b": 347.79318, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "automated methods. A second reason to use human annotations is", "bbox": {"l": 317.95499, "t": 350.37753, "r": 558.20062, "b": 358.75217, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "that the latter usually provide a more natural interpretation of the", "bbox": {"l": 317.95499, "t": 361.33554, "r": 558.20184, "b": 369.71017, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "page layout. The human-interpreted layout can significantly devi-", "bbox": {"l": 317.95499, "t": 372.29453, "r": 559.71442, "b": 380.66916, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "ate from the programmatic layout used in typesetting. For example,", "bbox": {"l": 317.95499, "t": 383.25354, "r": 559.1864, "b": 391.62817, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "\u201cinvisible\u201d tables might be used solely for aligning text paragraphs", "bbox": {"l": 316.94199, "t": 394.21252, "r": 558.20111, "b": 402.58716, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "on columns. Such typesetting tricks might be interpreted by au-", "bbox": {"l": 317.95499, "t": 405.17154, "r": 559.7132, "b": 413.54617, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "tomated methods incorrectly as an actual table, while the human", "bbox": {"l": 317.95499, "t": 416.13052, "r": 558.20013, "b": 424.50515999999993, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "annotation will interpret it correctly as", "bbox": {"l": 317.95499, "t": 427.08953999999994, "r": 464.50613, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "Text", "bbox": {"l": 466.98199000000005, "t": 427.13439999999997, "r": 482.14560000000006, "b": 435.47313999999994, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "or other styles. The", "bbox": {"l": 485.13199000000003, "t": 427.08953999999994, "r": 558.19727, "b": 435.46417, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "same applies to multi-line text elements, when authors decided to", "bbox": {"l": 317.95499, "t": 438.04855, "r": 558.20221, "b": 446.4231899999999, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "space them as \u201cinvisible\u201d list elements without bullet symbols. A", "bbox": {"l": 317.95499, "t": 449.00754, "r": 558.51501, "b": 457.38217, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "third reason to gather ground-truth through human annotation is", "bbox": {"l": 317.95499, "t": 459.96655000000004, "r": 558.19855, "b": 468.34119, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy.", "bbox": {"l": 317.95499, "t": 470.92453, "r": 559.58215, "b": 479.29916, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "As we will show in Section 4, certain documents featuring complex", "bbox": {"l": 317.64099, "t": 481.88354, "r": 558.41559, "b": 490.25818, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "layouts can have different but equally acceptable layout interpre-", "bbox": {"l": 317.95499, "t": 492.84253, "r": 559.72156, "b": 501.21716, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "tations. This natural upper bound for segmentation accuracy can", "bbox": {"l": 317.95499, "t": 503.80154, "r": 558.19928, "b": 512.1761799999999, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "be found by annotating the same pages multiple times by different", "bbox": {"l": 317.95499, "t": 514.76053, "r": 558.20581, "b": 523.13516, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "people and evaluating the inter-annotator agreement. Such a base-", "bbox": {"l": 317.95499, "t": 525.71954, "r": 559.71729, "b": 534.09418, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "line consistency evaluation is very useful to define expectations", "bbox": {"l": 317.95499, "t": 536.6785600000001, "r": 558.20404, "b": 545.05318, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "for a good target accuracy in trained deep neural network models", "bbox": {"l": 317.95499, "t": 547.63756, "r": 558.20074, "b": 556.01218, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "bbox": {"l": 317.95499, "t": 558.59656, "r": 558.20062, "b": 566.97118, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "annotation consistency proved to be a key challenge in human", "bbox": {"l": 317.95499, "t": 569.55556, "r": 558.20416, "b": 577.9301800000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "annotation, as we outline in Section 4.", "bbox": {"l": 317.95499, "t": 580.51456, "r": 457.62469, "b": 588.88918, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, \u201cinvisible\u201d tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as \u201cinvisible\u201d list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a \u201cnatural\u201d upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.95499, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"id": 128, "text": "4", "bbox": {"l": 317.95499, "t": 606.84991, "r": 323.56226, "b": 617.15901, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "ANNOTATION CAMPAIGN", "bbox": {"l": 334.47134, "t": 606.84991, "r": 470.21326, "b": 617.15901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 631.97356, "r": 559.71381, "b": 706.102173, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"id": 130, "text": "The annotation campaign was carried out in four phases. In phase", "bbox": {"l": 317.686, "t": 631.97356, "r": 558.20148, "b": 640.34818, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "one, we identified and prepared the data sources for annotation.", "bbox": {"l": 317.95499, "t": 642.93256, "r": 559.58521, "b": 651.30717, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "In phase two, we determined the class labels and how annotations", "bbox": {"l": 317.95499, "t": 653.89156, "r": 558.20007, "b": 662.26617, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "should be done on the documents in order to obtain maximum con-", "bbox": {"l": 317.95499, "t": 664.85056, "r": 559.71375, "b": 673.22517, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "sistency. The latter was guided by a detailed requirement analysis", "bbox": {"l": 317.95499, "t": 675.80956, "r": 558.20233, "b": 684.18417, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "and exhaustive experiments. In phase three, we trained the annota-", "bbox": {"l": 317.95499, "t": 686.76855, "r": 559.71381, "b": 695.143173, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "tion staff and performed exams for quality assurance. In phase four,", "bbox": {"l": 317.95499, "t": 697.7275539999999, "r": 559.1864, "b": 706.102173, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. 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"coord_origin": "TOPLEFT"}}, {"id": 162, "text": "68-85", "bbox": {"l": 487.47034, "t": 283.48654, "r": 507.17849999999993, "b": 291.86118000000005, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "bbox": {"l": 53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}, {"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "95-99", 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"r": 324.98117, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "n/a", "bbox": {"l": 342.79739, "t": 184.45752000000005, "r": 354.65097, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "n/a", "bbox": {"l": 372.46719, "t": 184.45752000000005, "r": 384.32077, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "84-87", "bbox": {"l": 398.45181, "t": 184.45752000000005, "r": 418.15997, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "86-96", "bbox": {"l": 428.12164, "t": 184.45752000000005, "r": 447.8298, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "n/a", "bbox": {"l": 465.655, "t": 184.45752000000005, "r": 477.50857999999994, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "n/a", "bbox": {"l": 495.3248, "t": 184.45752000000005, "r": 507.17838000000006, "b": 192.83214999999996, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "List-item", 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Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "2.32", "bbox": {"l": 280.82812, "t": 162.53954999999996, "r": 295.30887, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "84-89", "bbox": {"l": 305.27301, "t": 162.53954999999996, "r": 324.98117, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "40-61", "bbox": {"l": 334.94284, "t": 162.53954999999996, "r": 354.651, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "86-92", "bbox": {"l": 364.61267, "t": 162.53954999999996, "r": 384.32083, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "94-99", "bbox": {"l": 398.45187, "t": 162.53954999999996, "r": 418.16003, "b": 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"assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 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The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"id": 2, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "bbox": {"l": 53.501999, "t": 84.95495999999991, "r": 558.48969, "b": 93.42822000000001, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "bbox": {"l": 53.79800000000001, "t": 95.91394000000003, "r": 558.20294, "b": 104.3872100000001, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "bbox": {"l": 53.79800000000001, "t": 106.87292000000002, "r": 469.84805000000006, "b": 115.34618999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row \u201cTotal\u201d) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"id": 5, "text": "% of Total", "bbox": {"l": 233.94400000000002, "t": 140.22351000000003, "r": 270.04272, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "triple inter-annotator mAP @ 0.5-0.95 (%)", "bbox": {"l": 329.04999, "t": 140.22351000000003, "r": 483.3976400000001, "b": 148.59813999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "class label", "bbox": {"l": 104.825, "t": 151.18255999999997, "r": 141.71277, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Count", "bbox": {"l": 175.94701, "t": 151.18255999999997, "r": 198.71269, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Train", "bbox": {"l": 213.795, "t": 151.18255999999997, "r": 233.69144, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Test", "bbox": {"l": 249.37367, "t": 151.18255999999997, "r": 264.5, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Val", "bbox": {"l": 283.53568, "t": 151.18255999999997, "r": 295.30856, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "All", "bbox": {"l": 314.01501, "t": 151.18255999999997, "r": 324.98093, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Fin", "bbox": {"l": 343.01236, "t": 151.18255999999997, "r": 354.65076, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Man", "bbox": {"l": 367.84033, "t": 151.18255999999997, "r": 384.32059, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Sci", "bbox": {"l": 407.54358, "t": 151.18255999999997, "r": 418.15979, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Law", "bbox": {"l": 432.29979999999995, "t": 151.18255999999997, "r": 447.82962, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Pat", "bbox": {"l": 465.72656, "t": 151.18255999999997, "r": 477.50842, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Ten", "bbox": {"l": 493.52240000000006, "t": 151.18255999999997, "r": 507.17822, "b": 159.55719, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Caption", "bbox": {"l": 104.825, "t": 162.53954999999996, "r": 134.01064, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "22524", "bbox": {"l": 177.866, "t": 162.53954999999996, "r": 198.71288, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.04", "bbox": {"l": 219.211, "t": 162.53954999999996, "r": 233.69174000000004, "b": 170.91418, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "1.77", "bbox": {"l": 250.01956, "t": 162.53954999999996, "r": 264.50031, "b": 170.91418, 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53.79800000000001, "t": 554.00999, "r": 295.64871, "b": 562.48325, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "face. The PDF page is shown in the background, with over-", "bbox": {"l": 53.79800000000001, "t": 564.96899, "r": 295.64874, "b": 573.44225, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "laid text-cells (in darker shades). The annotation boxes can", "bbox": {"l": 53.79800000000001, "t": 575.92799, "r": 294.04376, "b": 584.40125, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "be drawn by dragging a rectangle over each segment with", "bbox": {"l": 53.79800000000001, "t": 586.88699, "r": 294.04373, "b": 595.36024, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "the respective label from the palette on the right.", "bbox": {"l": 53.79800000000001, "t": 597.84599, "r": 252.78931000000003, "b": 606.31924, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 675.54317, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"id": 168, "text": "we distributed the annotation workload and performed continuous", "bbox": {"l": 53.466999, "t": 634.29155, "r": 294.04745, "b": 642.66617, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "quality controls. Phase one and two required a small team of experts", "bbox": {"l": 53.79800000000001, "t": 645.25055, "r": 294.04535, "b": 653.62517, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "only. For phases three and four, a group of 40 dedicated annotators", "bbox": {"l": 53.79800000000001, "t": 656.20955, "r": 294.04422, "b": 664.58417, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "were assembled and supervised.", "bbox": {"l": 53.466999, "t": 667.16856, "r": 170.58611, "b": 675.54317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.79800000000001, "t": 678.01099, "r": 295.55844, "b": 708.420174, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"id": 172, "text": "Phase 1: Data selection and preparation.", "bbox": {"l": 63.76099800000001, "t": 678.01099, "r": 226.72533000000004, "b": 686.48424, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "Our inclusion cri-", "bbox": {"l": 229.06900000000002, "t": 678.12756, "r": 295.55844, "b": 686.50217, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "teria for documents were described in Section 3. A large effort went", "bbox": {"l": 53.79800000000001, "t": 689.08656, "r": 294.04538, "b": 697.461174, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "into ensuring that all documents are free to use. The data sources", "bbox": {"l": 53.79800000000001, "t": 700.045555, "r": 294.04642, "b": 708.420174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 375.24817, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"id": 176, "text": "include publication repositories such as arXiv$^{3}$, government offices,", "bbox": {"l": 317.95499, "t": 312.07953, "r": 559.18536, "b": 320.45416000000006, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "company websites as well as data directory services for financial", "bbox": {"l": 317.95499, "t": 323.03754, "r": 558.19843, "b": 331.41217, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "reports and patents. Scanned documents were excluded wherever", "bbox": {"l": 317.95499, "t": 333.99655, "r": 558.36963, "b": 342.37119, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "possible because they can be rotated or skewed. This would not", "bbox": {"l": 317.95499, "t": 344.95554, "r": 558.2041, "b": 353.33017, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "allow us to perform annotation with rectangular bounding-boxes", "bbox": {"l": 317.95499, "t": 355.91455, "r": 558.20294, "b": 364.28918, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and therefore complicate the annotation process.", "bbox": {"l": 317.95499, "t": 366.87354, "r": 496.71826, "b": 375.24817, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.95499, "t": 377.8325500000001, "r": 559.71307, "b": 506.75516, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"id": 182, "text": "Preparation work included uploading and parsing the sourced", "bbox": {"l": 327.918, "t": 377.8325500000001, "r": 558.20618, "b": 386.20717999999994, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "bbox": {"l": 317.95499, "t": 388.79153, "r": 558.2019, "b": 397.16617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "cloud-native platform which provides a visual annotation interface", "bbox": {"l": 317.95499, "t": 399.75055, "r": 558.20233, "b": 408.12518, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "and allows for dataset inspection and analysis. The annotation in-", "bbox": {"l": 317.95499, "t": 410.7095299999999, "r": 559.71277, "b": 419.08417, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "bbox": {"l": 317.95499, "t": 421.66855000000004, "r": 558.20062, "b": 430.04318, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "between the different document categories was achieved by se-", "bbox": {"l": 317.95499, "t": 432.62753, "r": 559.71307, "b": 441.00217, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "lective subsampling of pages with certain desired properties. For", "bbox": {"l": 317.95499, "t": 443.58554, "r": 558.36877, "b": 451.96017, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "example, we made sure to include the title page of each document", "bbox": {"l": 317.95499, "t": 454.54453, "r": 558.20428, "b": 462.91916, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and bias the remaining page selection to those with figures or", "bbox": {"l": 317.95499, "t": 465.50354, "r": 558.36877, "b": 473.87817, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "tables. The latter was achieved by leveraging pre-trained object", "bbox": {"l": 317.95499, "t": 476.46252, "r": 558.20428, "b": 484.83716, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "detection models from PubLayNet, which helped us estimate how", "bbox": {"l": 317.95499, "t": 487.42154, "r": 558.5307, "b": 495.79617, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "many figures and tables a given page contains.", "bbox": {"l": 317.95499, "t": 498.38052, "r": 488.46914999999996, "b": 506.75516, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 509.22299, "r": 559.71765, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"id": 194, "text": "Phase 2: Label selection and guideline.", "bbox": {"l": 327.918, "t": 509.22299, "r": 482.41809, "b": 517.69623, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "We reviewed the col-", "bbox": {"l": 484.474, "t": 509.33954, "r": 559.71765, "b": 517.71417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "lected documents and identified the most common structural fea-", "bbox": {"l": 317.95499, "t": 520.29855, "r": 559.71619, "b": 528.67319, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "bbox": {"l": 317.95499, "t": 531.2575400000001, "r": 558.20239, "b": 539.63217, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "elements and lead us to the definition of 11 distinct class labels.", "bbox": {"l": 317.95499, "t": 542.21655, "r": 559.58502, "b": 550.59117, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "These 11 class labels are", "bbox": {"l": 317.686, "t": 553.17456, "r": 404.14197, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "Caption", "bbox": {"l": 406.26599, "t": 553.21939, "r": 433.84860000000003, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": ",", "bbox": {"l": 433.84799, "t": 553.17456, "r": 435.78115999999994, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "Footnote", "bbox": {"l": 437.9079899999999, "t": 553.21939, "r": 467.23926, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": ",", "bbox": {"l": 467.23999, "t": 553.17456, "r": 469.17316000000005, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Formula", "bbox": {"l": 471.29900999999995, "t": 553.21939, "r": 500.44574000000006, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": ",", "bbox": {"l": 500.44601000000006, "t": 553.17456, "r": 502.37918, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "List-item", "bbox": {"l": 504.505, "t": 553.21939, "r": 535.74304, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": ",", "bbox": {"l": 535.74298, "t": 553.17456, "r": 537.67615, "b": 561.54918, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "Page-", "bbox": {"l": 539.802, "t": 553.21939, "r": 559.09839, "b": 561.5581500000001, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "footer", "bbox": {"l": 317.95499, "t": 564.17839, "r": 338.80725, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": ",", "bbox": {"l": 338.806, "t": 564.13356, "r": 340.81805, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "Page-header", "bbox": {"l": 343.61401, "t": 564.17839, "r": 387.96164, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": ",", "bbox": {"l": 387.961, "t": 564.13356, "r": 389.97305, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 392.76901, "t": 564.17839, "r": 417.84662, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 417.84799, "t": 564.13356, "r": 419.86005, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 215, "text": "Section-header", "bbox": {"l": 422.655, "t": 564.17839, "r": 475.56305, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 216, "text": ",", "bbox": {"l": 475.56201, "t": 564.13356, "r": 477.57407, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 217, "text": "Table", "bbox": {"l": 480.36899, "t": 564.17839, "r": 499.82196, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 218, "text": ",", "bbox": {"l": 499.8219900000001, "t": 564.13356, "r": 501.83405, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 219, "text": "Text", "bbox": {"l": 504.6290000000001, "t": 564.17839, "r": 519.7926, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 220, "text": ", and", "bbox": {"l": 519.79602, "t": 564.13356, "r": 538.37103, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 221, "text": "Title", "bbox": {"l": 541.16302, "t": 564.17839, "r": 557.57043, "b": 572.51715, "coord_origin": "TOPLEFT"}}, {"id": 222, "text": ".", "bbox": {"l": 557.57098, "t": 564.13356, "r": 559.58307, "b": 572.50818, "coord_origin": "TOPLEFT"}}, {"id": 223, "text": "Critical factors that were considered for the choice of these class", "bbox": {"l": 317.95499, "t": 575.09256, "r": 558.20416, "b": 583.46718, "coord_origin": "TOPLEFT"}}, {"id": 224, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "bbox": {"l": 317.95499, "t": 586.05156, "r": 558.43091, "b": 594.4261799999999, "coord_origin": "TOPLEFT"}}, {"id": 225, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "bbox": {"l": 317.95499, "t": 597.0105599999999, "r": 558.36871, "b": 605.38518, "coord_origin": "TOPLEFT"}}, {"id": 226, "text": "context from previous or next page) and (4) overall coverage of the", "bbox": {"l": 317.95499, "t": 607.96956, "r": 558.20105, "b": 616.3441799999999, "coord_origin": "TOPLEFT"}}, {"id": 227, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "bbox": {"l": 317.95499, "t": 618.9285600000001, "r": 559.18665, "b": 627.30318, "coord_origin": "TOPLEFT"}}, {"id": 228, "text": "while coverage ensures that all meaningful items on a page can", "bbox": {"l": 317.62299, "t": 629.88756, "r": 558.20142, "b": 638.26218, "coord_origin": "TOPLEFT"}}, {"id": 229, "text": "be annotated. We refrained from class labels that are very specific", "bbox": {"l": 317.95499, "t": 640.84656, "r": 558.20227, "b": 649.22118, "coord_origin": "TOPLEFT"}}, {"id": 230, "text": "to a document category, such as", "bbox": {"l": 317.95499, "t": 651.80556, "r": 436.90649, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 231, "text": "Abstract", "bbox": {"l": 439.13800000000003, "t": 651.8503900000001, "r": 469.69134999999994, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 232, "text": "in the", "bbox": {"l": 472.42898999999994, "t": 651.80556, "r": 493.97348, "b": 660.1801800000001, "coord_origin": "TOPLEFT"}}, {"id": 233, "text": "Scientific Articles", "bbox": {"l": 496.207, "t": 651.8503900000001, "r": 558.20001, "b": 660.18915, "coord_origin": "TOPLEFT"}}, {"id": 234, "text": "category. We also avoided class labels that are tightly linked to the", "bbox": {"l": 317.95499, "t": 662.76456, "r": 558.20557, "b": 671.13918, "coord_origin": "TOPLEFT"}}, {"id": 235, "text": "semantics of the text. Labels such as", "bbox": {"l": 317.95499, "t": 673.7225599999999, "r": 447.65221999999994, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 236, "text": "Author", "bbox": {"l": 449.85999, "t": 673.76739, "r": 474.31439, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 237, "text": "and", "bbox": {"l": 477.172, "t": 673.7225599999999, "r": 490.39655, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 238, "text": "Affiliation", "bbox": {"l": 492.60599, "t": 673.76739, "r": 528.29907, "b": 682.10614, "coord_origin": "TOPLEFT"}}, {"id": 239, "text": ", as seen", "bbox": {"l": 528.29901, "t": 673.7225599999999, "r": 558.20148, "b": 682.09718, "coord_origin": "TOPLEFT"}}, {"id": 240, "text": "in DocBank, are often only distinguishable by discriminating on", "bbox": {"l": 317.95499, "t": 684.68156, "r": 558.2041, "b": 693.0561749999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"id": 241, "text": "$^{3}$https://arxiv.org/", "bbox": {"l": 317.95499, "t": 702.353363, "r": 369.2457, "b": 708.86689, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "$^{3}$https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264115214347839, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 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"Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 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"children": []}, {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873711824417114, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264115214347839, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "A", "bbox": {"l": 322.19424, "t": 98.34105999999997, "r": 326.01498, "b": 104.25214000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "B", "bbox": {"l": 322.19424, "t": 186.99103000000002, "r": 326.01498, "b": 192.90204000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873711824417114, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"id": 2, "text": "the textual content of an element, which goes beyond visual layout", "bbox": {"l": 53.79800000000001, "t": 87.36352999999997, "r": 294.04541, "b": 95.73816, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "recognition, in particular outside the", "bbox": {"l": 53.79800000000001, "t": 98.32250999999997, "r": 188.1326, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Scientific Articles", "bbox": {"l": 190.37401, "t": 98.36737000000005, "r": 251.25586999999996, "b": 106.70612000000006, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "category.", "bbox": {"l": 253.70801, "t": 98.32250999999997, "r": 287.01816, "b": 106.69713999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.79800000000001, "t": 109.28156000000001, "r": 295.55923, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"id": 6, "text": "At first sight, the task of visual document-layout interpretation", "bbox": {"l": 63.76100199999999, "t": 109.28156000000001, "r": 294.04257, "b": 117.65618999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "appears intuitive enough to obtain plausible annotations in most", "bbox": {"l": 53.79800000000001, "t": 120.24054000000001, "r": 294.04266, "b": 128.61517000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "cases. However, during early trial-runs in the core team, we ob-", "bbox": {"l": 53.79800000000001, "t": 131.19854999999995, "r": 295.55615, "b": 139.57317999999998, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "served many cases in which annotators use different annotation", "bbox": {"l": 53.79800000000001, "t": 142.15752999999995, "r": 294.04715, "b": 150.53216999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "styles, especially for documents with challenging layouts. For ex-", "bbox": {"l": 53.79800000000001, "t": 153.11652000000004, "r": 295.55923, "b": 161.49114999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ample, if a figure is presented with subfigures, one annotator might", "bbox": {"l": 53.79800000000001, "t": 164.07556, "r": 294.04535, "b": 172.4502, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "draw a single figure bounding-box, while another might annotate", "bbox": {"l": 53.79800000000001, "t": 175.03454999999997, "r": 294.04803, "b": 183.40918, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "each subfigure separately. The same applies for lists, where one", "bbox": {"l": 53.79800000000001, "t": 185.99352999999996, "r": 294.04709, "b": 194.36816, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "might annotate all list items in one block or each list item sep-", "bbox": {"l": 53.79800000000001, "t": 196.95250999999996, "r": 295.55612, "b": 205.32714999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "arately. In essence, we observed that challenging layouts would", "bbox": {"l": 53.79800000000001, "t": 207.91156, "r": 294.04712, "b": 216.28619000000003, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "be annotated in different but plausible ways. To illustrate this, we", "bbox": {"l": 53.79800000000001, "t": 218.87054, "r": 294.04495, "b": 227.24518, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "bbox": {"l": 53.79800000000001, "t": 229.82952999999998, "r": 294.04712, "b": 238.20416, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "annotations on the same pages.", "bbox": {"l": 53.79800000000001, "t": 240.78754000000004, "r": 168.61276, "b": 249.16216999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.79800000000001, "t": 251.74652000000003, "r": 295.56006, "b": 336.83417, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"id": 19, "text": "Obviously, this inconsistency in annotations is not desirable for", "bbox": {"l": 63.76100199999999, "t": 251.74652000000003, "r": 294.21884, "b": 260.12114999999994, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "datasets which are intended to be used for model training. To min-", "bbox": {"l": 53.79800000000001, "t": 262.70556999999997, "r": 295.56006, "b": 271.0802, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "imise these inconsistencies, we created a detailed annotation guide-", "bbox": {"l": 53.79800000000001, "t": 273.66454999999996, "r": 295.55676, "b": 282.03918, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "line. While perfect consistency across 40 annotation staff members", "bbox": {"l": 53.79800000000001, "t": 284.62354, "r": 294.04922, "b": 292.99817, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "is clearly not possible to achieve, we saw a huge improvement in", "bbox": {"l": 53.79800000000001, "t": 295.5825500000001, "r": 294.04343, "b": 303.95717999999994, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "annotation consistency after the introduction of our annotation", "bbox": {"l": 53.79800000000001, "t": 306.54153, "r": 294.04712, "b": 314.91617, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "guideline. A few selected, non-trivial highlights of the guideline", "bbox": {"l": 53.79800000000001, "t": 317.50055, "r": 294.04718, "b": 325.87518, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "are:", "bbox": {"l": 53.79800000000001, "t": 328.4595299999999, "r": 67.28347, "b": 336.83417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.51254, "r": 294.0462, "b": 389.77313, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"id": 27, "text": "(1)", "bbox": {"l": 64.708, "t": 348.51254, "r": 74.626793, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Every list-item is an individual object instance with class", "bbox": {"l": 76.963936, "t": 348.51254, "r": 294.0462, "b": 356.88718, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "label", "bbox": {"l": 78.207001, "t": 359.47153, "r": 95.730484, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "List-item", "bbox": {"l": 97.976997, "t": 359.51639, "r": 130.17131, "b": 367.85513, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": ". This definition is different from PubLayNet", "bbox": {"l": 130.17101, "t": 359.47153, "r": 294.04053, "b": 367.84616, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "and DocBank, where all list-items are grouped together into", "bbox": {"l": 78.207001, "t": 370.43054, "r": 294.04385, "b": 378.80518, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "one", "bbox": {"l": 78.207001, "t": 381.38953000000004, "r": 91.593834, "b": 389.76416, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "List", "bbox": {"l": 93.834999, "t": 381.43439000000006, "r": 106.88111, "b": 389.77313, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "object.", "bbox": {"l": 109.629, "t": 381.38953000000004, "r": 133.72173, "b": 389.76416, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.707993, "t": 392.34851, "r": 295.56372, "b": 433.60016, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"id": 36, "text": "(2)", "bbox": {"l": 64.707993, "t": 392.34851, "r": 75.097656, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "A", "bbox": {"l": 77.545731, "t": 392.34851, "r": 84.351402, "b": 400.72313999999994, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "List-item", "bbox": {"l": 86.584, "t": 392.39339999999993, "r": 118.01329000000001, "b": 400.73215, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "is a paragraph with hanging indentation. Single-", "bbox": {"l": 120.26899999999999, "t": 392.34854, "r": 295.55695, "b": 400.72317999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "line elements can qualify as", "bbox": {"l": 78.207001, "t": 403.30753, "r": 181.80978, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "List-item", "bbox": {"l": 184.175, "t": 403.35239, "r": 216.68806000000004, "b": 411.69113, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "if the neighbour ele-", "bbox": {"l": 219.078, "t": 403.30753, "r": 295.56372, "b": 411.68216, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "ments expose hanging indentation. Bullet or enumeration", "bbox": {"l": 78.207001, "t": 414.2665400000001, "r": 294.04617, "b": 422.64116999999993, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "symbols are not a requirement.", "bbox": {"l": 78.207001, "t": 425.22552, "r": 192.00853, "b": 433.60016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 294.04724, "b": 455.52713, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"id": 45, "text": "(3)", "bbox": {"l": 64.708, "t": 436.18451000000005, "r": 74.483009, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "For every", "bbox": {"l": 76.786255, "t": 436.18451000000005, "r": 112.61566, "b": 444.55914, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Caption", "bbox": {"l": 114.861, "t": 436.2294, "r": 142.61249, "b": 444.56815000000006, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", there must be exactly one corresponding", "bbox": {"l": 142.612, "t": 436.18454, "r": 294.04724, "b": 444.55917, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Picture", "bbox": {"l": 78.207001, "t": 447.18839, "r": 102.79287, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "or", "bbox": {"l": 105.245, "t": 447.14352, "r": 113.09956999999999, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Table", "bbox": {"l": 115.341, "t": 447.18839, "r": 134.40356, "b": 455.52713, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": ".", "bbox": {"l": 134.403, "t": 447.14352, "r": 136.37561, "b": 455.51815999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 294.04599, "b": 477.43518, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"id": 53, "text": "(4)", "bbox": {"l": 64.708, "t": 458.1015300000001, "r": 74.220215, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Connected sub-pictures are grouped together in one", "bbox": {"l": 76.461555, "t": 458.1015300000001, "r": 267.46786, "b": 466.47617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Picture", "bbox": {"l": 269.70599, "t": 458.14639, "r": 294.04599, "b": 466.48514, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "object.", "bbox": {"l": 78.207001, "t": 469.06055, "r": 102.29972, "b": 477.43518, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.01953, "r": 264.50571, "b": 488.40314, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"id": 57, "text": "(5)", "bbox": {"l": 64.708, "t": 480.01953, "r": 74.345383, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Formula numbers are included in a", "bbox": {"l": 76.616203, "t": 480.01953, "r": 206.13503, "b": 488.39417, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Formula", "bbox": {"l": 208.38, "t": 480.06439, "r": 238.12155, "b": 488.40314, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "object.", "bbox": {"l": 240.41300999999999, "t": 480.01953, "r": 264.50571, "b": 488.39417, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708008, "t": 490.97852, "r": 294.04617, "b": 521.27118, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"id": 61, "text": "(6)", "bbox": {"l": 64.708008, "t": 490.97852, "r": 74.564522, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "bbox": {"l": 76.886978, "t": 490.97852, "r": 294.04617, "b": 499.35315, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "a paragraph is not considered a", "bbox": {"l": 78.207001, "t": 501.93753, "r": 200.34819, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Section-header", "bbox": {"l": 203.66701, "t": 501.98239, "r": 256.57504, "b": 510.32114, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": ", unless it", "bbox": {"l": 256.57401, "t": 501.93753, "r": 294.04401, "b": 510.31216, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "appears exclusively on its own line.", "bbox": {"l": 78.207001, "t": 512.8965499999999, "r": 208.13017, "b": 521.27118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 295.56253, "b": 574.20117, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"id": 67, "text": "The complete annotation guideline is over 100 pages long and a", "bbox": {"l": 53.528999, "t": 532.9505300000001, "r": 294.04337, "b": 541.32516, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "detailed description is obviously out of scope for this paper. Never-", "bbox": {"l": 53.79800000000001, "t": 543.90855, "r": 295.56253, "b": 552.28317, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "theless, it will be made publicly available alongside with DocLayNet", "bbox": {"l": 53.79800000000001, "t": 554.8675499999999, "r": 294.04538, "b": 563.24217, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "for future reference.", "bbox": {"l": 53.79800000000001, "t": 565.82655, "r": 127.2418, "b": 574.20117, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.79800000000001, "t": 576.66899, "r": 295.56226, "b": 705.708176, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"id": 71, "text": "Phase 3: Training.", "bbox": {"l": 63.76100199999999, "t": 576.66899, "r": 136.7744, "b": 585.14224, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "After a first trial with a small group of peo-", "bbox": {"l": 139.008, "t": 576.7855500000001, "r": 295.56226, "b": 585.16017, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "ple, we realised that providing the annotation guideline and a set of", "bbox": {"l": 53.79800000000001, "t": 587.74455, "r": 294.04532, "b": 596.1191699999999, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "random practice pages did not yield the desired quality level for lay-", "bbox": {"l": 53.79800000000001, "t": 598.70355, "r": 295.55676, "b": 607.07817, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "out annotation. Therefore we prepared a subset of pages with two", "bbox": {"l": 53.79800000000001, "t": 609.66255, "r": 294.04605, "b": 618.0371700000001, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "different complexity levels, each with a practice and an exam part.", "bbox": {"l": 53.79800000000001, "t": 620.6215500000001, "r": 295.42377, "b": 628.99617, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "974 pages were reference-annotated by one proficient core team", "bbox": {"l": 53.79800000000001, "t": 631.58055, "r": 294.04712, "b": 639.95517, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "member. Annotation staff were then given the task to annotate the", "bbox": {"l": 53.79800000000001, "t": 642.53955, "r": 294.04922, "b": 650.91417, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "same subsets (blinded from the reference). By comparing the an-", "bbox": {"l": 53.79800000000001, "t": 653.49855, "r": 295.55618, "b": 661.87317, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "notations of each staff member with the reference annotations, we", "bbox": {"l": 53.79800000000001, "t": 664.45655, "r": 294.04874, "b": 672.83117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "could quantify how closely their annotations matched the reference.", "bbox": {"l": 53.79800000000001, "t": 675.4155499999999, "r": 295.42496, "b": 683.79017, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Only after passing two exam levels with high annotation quality,", "bbox": {"l": 53.79800000000001, "t": 686.37456, "r": 295.0274, "b": 694.749176, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "staff were admitted into the production phase. Practice iterations", "bbox": {"l": 53.79800000000001, "t": 697.333557, "r": 294.04114, "b": 705.708176, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264115214347839, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, 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"text", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "bbox": {"l": 340.00214, "t": 179.79296999999997, "r": 416.20551, "b": 181.90972999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Compliant with guidelines", "bbox": {"l": 339.38269, "t": 85.19066999999995, "r": 417.83722, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Plausible but invalid alternative", "bbox": {"l": 451.42834, "t": 85.19066999999995, "r": 546.22913, "b": 92.28399999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Borderline case: Two guideline-compliant alternatives", "bbox": {"l": 350.33701, "t": 364.85706, "r": 513.48035, "b": 371.95035000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "bbox": {"l": 340.00201, "t": 245.07385, "r": 416.20538, "b": 247.19061, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 340.00201, "t": 359.12488, "r": 416.20538, "b": 361.24167, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 93, "text": "C", "bbox": {"l": 322.19424, "t": 253.54192999999998, "r": 326.01498, "b": 259.453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 94, "text": "D", "bbox": {"l": 322.19424, "t": 367.08495999999997, "r": 326.01498, "b": 372.996, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "text", "id": 22, "page_no": 4, "cluster": {"id": 22, "label": "text", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "bbox": {"l": 400.12842, "t": 458.44327000000004, "r": 476.33178999999996, "b": 460.5600600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0"}, {"label": "caption", "id": 14, "page_no": 4, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 317.95499, "t": 473.49399, "r": 559.80579, "b": 503.88519, "coord_origin": "TOPLEFT"}, "confidence": 0.8873711824417114, "cells": [{"id": 95, "text": "Figure 4: Examples of plausible annotation alternatives for", "bbox": {"l": 317.95499, "t": 473.49399, "r": 558.3891, "b": 481.96722, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "the same page. Criteria in our annotation guideline can re-", "bbox": {"l": 317.95499, "t": 484.45297, "r": 559.80579, "b": 492.92621, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "solve cases A to C, while the case D remains ambiguous.", "bbox": {"l": 317.95499, "t": 495.41196, "r": 544.14148, "b": 503.88519, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 544.83118, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"id": 98, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "bbox": {"l": 317.62299, "t": 525.49753, "r": 558.20435, "b": 533.87216, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "40", "bbox": {"l": 317.74899, "t": 536.45656, "r": 326.07019, "b": 544.83118, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "initially allocated annotators did not pass the bar.", "bbox": {"l": 328.3071, "t": 536.45656, "r": 509.11896, "b": 544.83118, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.62299, "t": 547.299, "r": 559.7149, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"id": 101, "text": "Phase 4: Production annotation.", "bbox": {"l": 327.918, "t": 547.299, "r": 456.80109000000004, "b": 555.77225, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "The previously selected 80K", "bbox": {"l": 458.7120100000001, "t": 547.41556, "r": 558.48926, "b": 555.79018, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "bbox": {"l": 317.95499, "t": 558.37456, "r": 559.71368, "b": 566.74918, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "tors. This production phase took around three months to complete.", "bbox": {"l": 317.95499, "t": 569.33356, "r": 559.58124, "b": 577.7081800000001, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "All annotations were created online through CCS, which visualises", "bbox": {"l": 317.64099, "t": 580.29256, "r": 558.20386, "b": 588.66718, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "bbox": {"l": 317.95499, "t": 591.25156, "r": 558.20221, "b": 599.62617, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "bbox": {"l": 317.95499, "t": 602.20955, "r": 559.18457, "b": 610.58417, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "as shown in Figure 3. With regard to the annotation practices, we", "bbox": {"l": 317.95499, "t": 613.16855, "r": 558.20197, "b": 621.54317, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "implemented a few constraints and capabilities on the tooling level.", "bbox": {"l": 317.95499, "t": 624.12755, "r": 559.58197, "b": 632.50217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "bbox": {"l": 317.95499, "t": 635.08655, "r": 559.71411, "b": 643.46117, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "lar boxes. For the large majority of documents, this constraint was", "bbox": {"l": 317.95499, "t": 646.04555, "r": 558.20557, "b": 654.42017, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "sufficient and it speeds up the annotation considerably in compar-", "bbox": {"l": 317.95499, "t": 657.00455, "r": 559.7149, "b": 665.3791699999999, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "bbox": {"l": 317.95499, "t": 667.9635499999999, "r": 558.19849, "b": 676.33817, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "were not able to see each other\u2019s annotations. This was enforced by", "bbox": {"l": 317.62299, "t": 678.92255, "r": 558.43268, "b": 687.29717, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "design to avoid any bias in the annotation, which could skew the", "bbox": {"l": 317.95499, "t": 689.8815500000001, "r": 558.19806, "b": 698.256172, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "bbox": {"l": 317.95499, "t": 700.840553, "r": 558.20227, "b": 709.2151719999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other\u2019s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "YOLO", "bbox": {"l": 258.03125, "t": 197.97351000000003, "r": 280.17825, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "R50", "bbox": {"l": 168.39301, "t": 208.93255999999997, "r": 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349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "73.4", "bbox": {"l": 226.86324000000002, "t": 341.23654, "r": 241.34396, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "76.8", "bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 507.85602, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "100", "bbox": {"l": 537.99524, "t": 246.68017999999995, "r": 547.94318, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "% of DocLayNet training set", "bbox": {"l": 410.28143, "t": 253.80840999999998, "r": 483.47278000000006, "b": 259.88251, "coord_origin": "TOPLEFT"}}, {"id": 138, "text": "50", "bbox": {"l": 330.93539, "t": 218.38464, "r": 337.56735, "b": 224.45874000000003, "coord_origin": "TOPLEFT"}}, {"id": 139, "text": "55", "bbox": {"l": 330.93539, "t": 192.08660999999995, "r": 337.56735, "b": 198.16071, "coord_origin": "TOPLEFT"}}, {"id": 140, "text": "60", "bbox": {"l": 330.93539, "t": 165.78864, "r": 337.56735, "b": 171.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 141, "text": "65", "bbox": {"l": 330.93539, "t": 139.49059999999997, "r": 337.56735, "b": 145.56470000000002, "coord_origin": "TOPLEFT"}}, {"id": 142, "text": "70", "bbox": {"l": 330.93539, "t": 113.19263000000001, "r": 337.56735, "b": 119.26671999999996, "coord_origin": "TOPLEFT"}}, {"id": 143, "text": "mAP 0.50:0.95", "bbox": {"l": 322.92276, "t": 148.37689, "r": 328.99686, "b": 186.79218000000003, "coord_origin": "TOPLEFT"}}, {"id": 144, "text": "10", "bbox": {"l": 470.97235, "t": 235.36676, "r": 477.6055, "b": 241.44086000000004, "coord_origin": "TOPLEFT"}}, {"id": 145, "text": "1", "bbox": {"l": 477.65662, "t": 234.82390999999996, "r": 479.97778000000005, "b": 239.07581000000005, "coord_origin": "TOPLEFT"}}, {"id": 146, "text": "10", "bbox": {"l": 531.55127, "t": 235.41234999999995, "r": 538.18445, "b": 241.48645, "coord_origin": "TOPLEFT"}}, {"id": 147, "text": "2", "bbox": {"l": 538.23553, "t": 234.86951, "r": 540.5567, "b": 239.1214, "coord_origin": "TOPLEFT"}}, {"id": 148, "text": "50", "bbox": {"l": 404.91125, "t": 216.00005999999996, "r": 411.54321, "b": 222.07416, "coord_origin": "TOPLEFT"}}, {"id": 149, "text": "55", "bbox": {"l": 404.91125, "t": 200.22125000000005, "r": 411.54321, "b": 206.29534999999998, "coord_origin": "TOPLEFT"}}, {"id": 150, "text": "60", "bbox": {"l": 404.91125, "t": 184.44244000000003, "r": 411.54321, "b": 190.51653999999996, "coord_origin": "TOPLEFT"}}, {"id": 151, "text": "65", "bbox": {"l": 404.91125, "t": 168.66364, "r": 411.54321, "b": 174.73773000000006, "coord_origin": "TOPLEFT"}}, {"id": 152, "text": "70", "bbox": {"l": 404.91125, "t": 152.88489000000004, "r": 411.54321, "b": 158.95898, "coord_origin": "TOPLEFT"}}, {"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}, {"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}, {"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}, {"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662876486778259, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 62.02753829956055, "t": 195.68006896972656, "r": 285.78955078125, "b": 351.6618957519531, "coord_origin": "TOPLEFT"}, "confidence": 0.9883638620376587, "cells": [{"id": 13, "text": "human", "bbox": {"l": 132.36501, "t": 197.97351000000003, "r": 157.99098, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "MRCNN", "bbox": {"l": 173.505, "t": 197.97351000000003, "r": 204.61841, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "FRCNN", "bbox": {"l": 220.13028, "t": 197.97351000000003, "r": 248.06958, "b": 206.34813999999994, "coord_origin": "TOPLEFT"}}, {"id": 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Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"id": 131, "text": "0", "bbox": {"l": 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The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 261.86804, "t": 341.23654, "r": 276.34879, "b": 349.61118000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.528999, "t": 370.92755, "r": 295.55612, "b": 576.56317, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"id": 93, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "bbox": {"l": 53.79800000000001, "t": 370.92755, "r": 294.04712, "b": 379.30219000000005, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "numbers for human document-layout annotation. Third, we in-", "bbox": {"l": 53.79800000000001, "t": 381.88654, "r": 295.55612, "b": 390.26117, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "troduced the feature of", "bbox": {"l": 53.79800000000001, "t": 392.84555, "r": 140.3623, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "snapping", "bbox": {"l": 142.99001, "t": 392.89041, "r": 175.9695, "b": 401.2291599999999, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "boxes around text segments to", "bbox": {"l": 178.951, "t": 392.84555, "r": 294.04083, "b": 401.22017999999997, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "bbox": {"l": 53.79800000000001, "t": 403.80453, "r": 295.42493, "b": 412.17917, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "The CCS annotation tool automatically shrinks every user-drawn", "bbox": {"l": 53.528999, "t": 414.76355, "r": 294.04251, "b": 423.13818, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "box to the minimum bounding-box around the enclosed text-cells", "bbox": {"l": 53.79800000000001, "t": 425.72253, "r": 294.04807, "b": 434.09717, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "for all purely text-based segments, which excludes only", "bbox": {"l": 53.79800000000001, "t": 436.6815500000001, "r": 256.80627, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "Table", "bbox": {"l": 259.04199, "t": 436.72641, "r": 278.10455, "b": 445.06516, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "and", "bbox": {"l": 280.54999, "t": 436.6815500000001, "r": 294.04443, "b": 445.0561799999999, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Picture", "bbox": {"l": 53.79800000000001, "t": 447.68539, "r": 78.875587, "b": 456.02413999999993, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": ". For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9639935493469238, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64874, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"id": 2, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.04361, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "detection networks on DocLayNet test set. The MRCNN", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04373, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "bbox": {"l": 53.52, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "the network architectures from the", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 202.43402, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "detectron2", "bbox": {"l": 206.08501, "t": 130.71783000000005, "r": 247.14215000000002, "b": 139.20905000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "model zoo", "bbox": {"l": 250.95001, "t": 130.70885999999996, "r": 294.04254, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "bbox": {"l": 53.52002, "t": 141.66785000000004, "r": 294.04367, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "3x), with default configurations. The YOLO implementation", "bbox": {"l": 53.798019, "t": 152.62683000000004, "r": 294.04373, "b": 161.1001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "bbox": {"l": 53.798019, "t": 163.58582, "r": 295.64874, "b": 172.05908, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "ing pre-trained weights from the COCO 2017 dataset.", "bbox": {"l": 53.798019, "t": 174.54381999999998, "r": 268.62399, "b": 183.01709000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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For the latter, we instructed annotation staff to minimise", "bbox": {"l": 78.876999, "t": 447.64053, "r": 294.04852, "b": 456.01517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "inclusion of surrounding whitespace while including all graphical", "bbox": {"l": 53.79800000000001, "t": 458.59955, "r": 294.04645, "b": 466.97418, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "bbox": {"l": 53.79800000000001, "t": 469.55853, "r": 294.0416, "b": 477.93317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "bbox": {"l": 53.79800000000001, "t": 480.51654, "r": 294.04538, "b": 488.89117, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "need to be skipped. Fourth, we established a way to flag pages as", "bbox": {"l": 53.79800000000001, "t": 491.47552, "r": 294.04312, "b": 499.85016, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rejected", "bbox": {"l": 53.79800000000001, "t": 502.4794, "r": 80.597939, "b": 510.81815, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "for cases where no valid annotation according to the label", "bbox": {"l": 83.366997, "t": 502.43454, "r": 294.04483, "b": 510.80917, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "guidelines could be achieved. Example cases for this would be PDF", "bbox": {"l": 53.79800000000001, "t": 513.3935200000001, "r": 294.25833, "b": 521.7681600000001, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "pages that render incorrectly or contain layouts that are impossible", "bbox": {"l": 53.79800000000001, "t": 524.35254, "r": 294.04535, "b": 532.72717, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "bbox": {"l": 53.79800000000001, "t": 535.31155, "r": 294.04535, "b": 543.68617, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "not contained in the final dataset. With all these measures in place,", "bbox": {"l": 53.79800000000001, "t": 546.27055, "r": 295.02759, "b": 554.64517, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "experienced annotation staff managed to annotate a single page in", "bbox": {"l": 53.79800000000001, "t": 557.22955, "r": 294.0488, "b": 565.6041700000001, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "bbox": {"l": 53.79800000000001, "t": 568.18855, "r": 281.80457, "b": 576.56317, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"id": 118, "text": "5", "bbox": {"l": 53.79800000000001, "t": 588.12991, "r": 59.405277, "b": 598.43901, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "EXPERIMENTS", "bbox": {"l": 70.314377, "t": 588.12991, "r": 147.48535, "b": 598.43901, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484001, "t": 613.25356, "r": 295.4281, "b": 709.299171, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"id": 120, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "bbox": {"l": 53.528999, "t": 613.25356, "r": 294.04871, "b": 621.6281700000001, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "capable of accurate document-layout analysis on a wide variety", "bbox": {"l": 53.79800000000001, "t": 624.21255, "r": 294.27576, "b": 632.58717, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "of challenging layouts. As discussed in Section 2, object detection", "bbox": {"l": 53.79800000000001, "t": 635.17155, "r": 294.04144, "b": 643.54617, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "models are currently the easiest to use, due to the standardisation", "bbox": {"l": 53.79800000000001, "t": 646.13055, "r": 294.04163, "b": 654.50517, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "of ground-truth data in COCO format [16] and the availability of", "bbox": {"l": 53.79800000000001, "t": 657.0885499999999, "r": 294.0412, "b": 665.46317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "general frameworks such as", "bbox": {"l": 53.79800000000001, "t": 668.04755, "r": 155.01054, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "detectron2", "bbox": {"l": 157.23599, "t": 668.09238, "r": 193.26666, "b": 676.43114, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "[17]. Furthermore, baseline", "bbox": {"l": 195.867, "t": 668.04755, "r": 294.0473, "b": 676.42217, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "numbers in PubLayNet and DocBank were obtained using standard", "bbox": {"l": 53.79800000000001, "t": 679.0065500000001, "r": 294.04538, "b": 687.38117, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "bbox": {"l": 53.79800000000001, "t": 689.96555, "r": 295.4281, "b": 698.3401719999999, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "As such, we will relate to these object detection methods in this", "bbox": {"l": 53.484001, "t": 700.9245530000001, "r": 294.04413, "b": 709.299171, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"id": 131, "text": "0", "bbox": {"l": 349.16577, "t": 246.68017999999995, "r": 352.48175, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "20", "bbox": {"l": 385.93698, "t": 246.68017999999995, "r": 392.56894, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "40", "bbox": {"l": 424.366, "t": 246.68017999999995, "r": 430.99796, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "60", "bbox": {"l": 462.79504000000003, "t": 246.68017999999995, "r": 469.427, "b": 252.75427000000002, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "80", "bbox": {"l": 501.22406, "t": 246.68017999999995, "r": 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null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.95499, "t": 279.01599, "r": 559.80579, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9639935493469238, "cells": [{"id": 153, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 317.95499, "t": 279.01599, "r": 558.47876, "b": 287.48923, "coord_origin": "TOPLEFT"}}, {"id": 154, "text": "R-CNN network with ResNet50 backbone trained on increas-", "bbox": {"l": 317.95499, "t": 289.97501, "r": 559.80579, "b": 298.44824, "coord_origin": "TOPLEFT"}}, {"id": 155, "text": "ing fractions of the DocLayNet dataset. The learning curve", "bbox": {"l": 317.95499, "t": 300.93399, "r": 558.20068, "b": 309.40723, "coord_origin": "TOPLEFT"}}, {"id": 156, "text": "flattens around the 80% mark, indicating that increasing the", "bbox": {"l": 317.95499, "t": 311.89297, "r": 558.20062, "b": 320.36620999999997, "coord_origin": "TOPLEFT"}}, {"id": 157, "text": "size of the DocLayNet dataset with similar data will not yield", "bbox": {"l": 317.95499, "t": 322.85196, "r": 558.20074, "b": 331.3252, "coord_origin": "TOPLEFT"}}, {"id": 158, "text": "significantly better predictions.", "bbox": {"l": 317.95499, "t": 333.81094, "r": 445.24207, "b": 342.28418000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"id": 159, "text": "paper and leave the detailed evaluation of more recent methods", "bbox": {"l": 317.95499, "t": 384.01154, "r": 558.20416, "b": 392.38617, "coord_origin": "TOPLEFT"}}, {"id": 160, "text": "mentioned in Section 2 for future work.", "bbox": {"l": 317.95499, "t": 394.97055, "r": 463.04938, "b": 403.34517999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.64099, "t": 405.92953, "r": 558.4364, "b": 480.05716, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"id": 161, "text": "In this section, we will present several aspects related to the", "bbox": {"l": 327.918, "t": 405.92953, "r": 558.19836, "b": 414.30417, "coord_origin": "TOPLEFT"}}, {"id": 162, "text": "performance of object detection models on DocLayNet. Similarly", "bbox": {"l": 317.95499, "t": 416.88855, "r": 558.4364, "b": 425.26318, "coord_origin": "TOPLEFT"}}, {"id": 163, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "bbox": {"l": 317.95499, "t": 427.84653, "r": 558.20563, "b": 436.22116, "coord_origin": "TOPLEFT"}}, {"id": 164, "text": "using mean average precision (mAP) with 10 overlaps that range", "bbox": {"l": 317.95499, "t": 438.80554, "r": 558.20044, "b": 447.18018, "coord_origin": "TOPLEFT"}}, {"id": 165, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "bbox": {"l": 317.95499, "t": 449.76453000000004, "r": 558.19891, "b": 458.13916, "coord_origin": "TOPLEFT"}}, {"id": 166, "text": "computed by leveraging the evaluation code provided by the COCO", "bbox": {"l": 317.95499, "t": 460.72354, "r": 558.20239, "b": 469.09818, "coord_origin": "TOPLEFT"}}, {"id": 167, "text": "API [16].", "bbox": {"l": 317.64099, "t": 471.68253, "r": 350.32352, "b": 480.05716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"id": 168, "text": "Baselines for Object Detection", "bbox": {"l": 317.95499, "t": 496.8219, "r": 466.8532400000001, "b": 507.13098, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.74899, "t": 512.02454, "r": 558.43085, "b": 706.700172, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"id": 169, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "bbox": {"l": 317.95499, "t": 512.02454, "r": 558.43085, "b": 520.39917, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "bbox": {"l": 317.95499, "t": 522.9835499999999, "r": 558.20117, "b": 531.35818, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "and evaluation were performed on RGB images with dimensions of", "bbox": {"l": 317.95499, "t": 533.94254, "r": 558.20233, "b": 542.31717, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "1025", "bbox": {"l": 317.74899, "t": 544.90155, "r": 334.09296, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "\u00d7", "bbox": {"l": 334.81201, "t": 544.84775, "r": 340.51465, "b": 552.5499, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "1025 pixels. For training, we only used one annotation in case", "bbox": {"l": 341.233, "t": 544.90155, "r": 558.20117, "b": 553.27617, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "of redundantly annotated pages. As one can observe, the variation", "bbox": {"l": 317.95499, "t": 555.85956, "r": 558.20392, "b": 564.23418, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "in mAP between the models is rather low, but overall between 6", "bbox": {"l": 317.95499, "t": 566.8185599999999, "r": 558.2041, "b": 575.19318, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "and 10% lower than the mAP computed from the pairwise human", "bbox": {"l": 317.95499, "t": 577.77756, "r": 558.2052, "b": 586.15218, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "annotations on triple-annotated pages. This gives a good indication", "bbox": {"l": 317.95499, "t": 588.73656, "r": 558.20233, "b": 597.11118, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "bbox": {"l": 317.95499, "t": 599.69556, "r": 558.1983, "b": 608.0701799999999, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "research community to close the gap between human recognition", "bbox": {"l": 317.95499, "t": 610.65456, "r": 558.20502, "b": 619.02917, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "bbox": {"l": 317.95499, "t": 621.61356, "r": 558.20337, "b": 629.98817, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "bbox": {"l": 317.95499, "t": 632.5725600000001, "r": 558.2041, "b": 640.94717, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "that pixel-based image segmentation derived from bounding-boxes", "bbox": {"l": 317.95499, "t": 643.53156, "r": 558.20245, "b": 651.90617, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "does not help to obtain better predictions. On the other hand, the", "bbox": {"l": 317.95499, "t": 654.49055, "r": 558.19757, "b": 662.86517, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "more recent Yolov5x model does very well and even out-performs", "bbox": {"l": 317.95499, "t": 665.44855, "r": 558.20404, "b": 673.82317, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "humans on selected labels such as", "bbox": {"l": 317.95499, "t": 676.40755, "r": 444.54102, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Text", "bbox": {"l": 446.78900000000004, "t": 676.45238, "r": 461.95261000000005, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": ",", "bbox": {"l": 461.95599000000004, "t": 676.40755, "r": 463.96805000000006, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "Table", "bbox": {"l": 466.2170100000001, "t": 676.45238, "r": 485.66998000000007, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "and", "bbox": {"l": 488.1290000000001, "t": 676.40755, "r": 501.89330999999993, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "Picture", "bbox": {"l": 504.142, "t": 676.45238, "r": 529.21954, "b": 684.79114, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": ". This is", "bbox": {"l": 529.22101, "t": 676.40755, "r": 558.20392, "b": 684.78217, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "not entirely surprising, as", "bbox": {"l": 317.95499, "t": 687.36655, "r": 410.81366, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "Text", "bbox": {"l": 413.05301, "t": 687.41138, "r": 427.67865, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": ",", "bbox": {"l": 427.67801, "t": 687.36655, "r": 429.62103, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "Table", "bbox": {"l": 431.86099, "t": 687.41138, "r": 450.62881000000004, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "and", "bbox": {"l": 453.082, "t": 687.36655, "r": 466.37402, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "Picture", "bbox": {"l": 468.61499, "t": 687.41138, "r": 492.83208999999994, "b": 695.750137, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "are abundant and", "bbox": {"l": 495.28201, "t": 687.36655, "r": 558.2005, "b": 695.741173, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "the most visually distinctive in a document.", "bbox": {"l": 317.95499, "t": 698.325554, "r": 477.53903, "b": 706.700172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8662876486778259, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 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"b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "72", "bbox": {"l": 151.07401, "t": 285.40454, "r": 159.41275, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "73", "bbox": {"l": 177.23795, "t": 285.40454, "r": 185.57669, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "78", "bbox": {"l": 211.25645, "t": 285.40454, "r": 219.5952, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "77", "bbox": {"l": 245.27496, "t": 285.40454, "r": 253.61371, "b": 293.77917, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Learning Curve", "bbox": {"l": 53.79800000000001, "t": 319.56992, "r": 131.05624, "b": 329.879, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "Class-count", "bbox": {"l": 358.63901, "t": 153.10051999999996, "r": 401.73154, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "11", "bbox": {"l": 440.22501, "t": 153.10051999999996, "r": 448.56375, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "5", "bbox": {"l": 494.38, "t": 153.10051999999996, "r": 498.54938, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "Split", "bbox": {"l": 358.63901, "t": 164.05951000000005, "r": 375.27167, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Doc", "bbox": {"l": 423.341, "t": 164.05951000000005, "r": 438.0459, "b": 172.43413999999996, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "Page", 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"coord_origin": "TOPLEFT"}}, {"id": 168, "text": "84", "bbox": {"l": 452.56240999999994, "t": 296.36255, "r": 460.90115000000003, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 169, "text": "78", "bbox": {"l": 478.59399, "t": 296.36255, "r": 486.93274, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 170, "text": "87", "bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}}, {"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}, {"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}, {"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}, {"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}, {"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, 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To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 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The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 295.64865, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"id": 2, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.0437, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 294.04376, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "class label sets. The reduced label sets were obtained by ei-", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 295.64865, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ther down-mapping or dropping labels.", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 213.23856, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels."}, {"label": "table", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [{"id": 6, "text": "Class-count", "bbox": {"l": 86.372002, "t": 153.10051999999996, "r": 129.46452, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "11", "bbox": {"l": 151.07401, "t": 153.10051999999996, "r": 159.41275, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "6", "bbox": {"l": 179.31816, "t": 153.10051999999996, "r": 183.48753, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "5", "bbox": {"l": 213.33669, "t": 153.10051999999996, "r": 217.50606, "b": 161.47515999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "4", "bbox": {"l": 247.35521, "t": 153.10051999999996, 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"cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 334.77155, "r": 295.55835, "b": 529.44818, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"id": 72, "text": "One of the fundamental questions related to any dataset is if it is", "bbox": {"l": 53.79800000000001, "t": 334.77155, "r": 294.04153, "b": 343.14618, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "\u201clarge enough\u201d. To answer this question for DocLayNet, we per-", "bbox": {"l": 52.785, "t": 345.73053, "r": 295.55835, "b": 354.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "bbox": {"l": 53.79800000000001, "t": 356.6895400000001, "r": 294.04535, "b": 365.06418, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "model trained on increasing fractions of the DocLayNet dataset.", "bbox": {"l": 53.79800000000001, "t": 367.64853, "r": 295.4281, "b": 376.02316, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "bbox": {"l": 53.484001, "t": 378.60754, "r": 295.55667, "b": 386.98218, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "ginning and eventually levels out. To estimate the error-bar on the", "bbox": {"l": 53.79800000000001, "t": 389.56653, "r": 294.04865, "b": 397.94116, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "metrics, we ran the training five times on the entire data-set. This", "bbox": {"l": 53.79800000000001, "t": 400.52554000000003, "r": 294.04376, "b": 408.90018, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "bbox": {"l": 53.79800000000001, "t": 411.48456, "r": 295.42459, "b": 419.85919, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "In the inset of Figure 5, we show the exact same data-points, but", "bbox": {"l": 53.79800000000001, "t": 422.44354, "r": 294.04709, "b": 430.81818, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "bbox": {"l": 53.466999, "t": 433.40253000000007, "r": 294.04535, "b": 441.7771599999999, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "score increases linearly as a function of the data-size in the inset.", "bbox": {"l": 53.79800000000001, "t": 444.36053000000004, "r": 295.42902, "b": 452.73517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "bbox": {"l": 53.528999, "t": 455.31955, "r": 295.03122, "b": 463.69418, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "bbox": {"l": 53.466999, "t": 466.27853, "r": 295.42154, "b": 474.65317, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "This provides a good indication that the model would not improve", "bbox": {"l": 53.528999, "t": 477.23755, "r": 294.04553, "b": 485.61218, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "significantly by yet increasing the data size. Rather, it would prob-", "bbox": {"l": 53.79800000000001, "t": 488.19653, "r": 295.55646, "b": 496.57117, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "ably benefit more from improved data consistency (as discussed", "bbox": {"l": 53.79800000000001, "t": 499.15555, "r": 294.04715, "b": 507.53018, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "in Section 3), data augmentation methods [23], or the addition of", "bbox": {"l": 53.79800000000001, "t": 510.11453, "r": 294.04245, "b": 518.4891700000001, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "more document categories and styles.", "bbox": {"l": 53.79800000000001, "t": 521.0735500000001, "r": 191.47707, "b": 529.44818, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is \u201clarge enough\u201d. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"id": 90, "text": "Impact of Class Labels", "bbox": {"l": 53.79800000000001, "t": 542.50992, "r": 164.32898, "b": 552.81902, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 557.71155, "r": 295.55679, "b": 708.55217, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"id": 91, "text": "The choice and number of labels can have a significant effect on", "bbox": {"l": 53.528999, "t": 557.71155, "r": 294.04333, "b": 566.08617, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "the overall model performance. Since PubLayNet, DocBank and", "bbox": {"l": 53.79800000000001, "t": 568.6705499999999, "r": 294.04712, "b": 577.04517, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "DocLayNet all have different label sets, it is of particular interest to", "bbox": {"l": 53.79800000000001, "t": 579.62955, "r": 294.04538, "b": 588.0041699999999, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "understand and quantify this influence of the label set on the model", "bbox": {"l": 53.79800000000001, "t": 590.5885499999999, "r": 294.04535, "b": 598.96317, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "performance. We investigate this by either down-mapping labels", "bbox": {"l": 53.79800000000001, "t": 601.54755, "r": 294.04266, "b": 609.92216, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "into more common ones (e.g.", "bbox": {"l": 53.79800000000001, "t": 612.50656, "r": 163.59247, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Caption", "bbox": {"l": 166.26401, "t": 612.55139, "r": 194.97244, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "\u2192", "bbox": {"l": 194.994, "t": 612.45276, "r": 204.1756, "b": 620.15491, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "Text", "bbox": {"l": 204.17599, "t": 612.55139, "r": 219.33961000000002, "b": 620.89015, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": ") or excluding them", "bbox": {"l": 219.849, "t": 612.50656, "r": 294.04828, "b": 620.88118, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "from the annotations entirely. Furthermore, it must be stressed", "bbox": {"l": 53.79800000000001, "t": 623.46556, "r": 294.04709, "b": 631.84018, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "that all mappings and exclusions were performed on the data be-", "bbox": {"l": 53.79800000000001, "t": 634.42355, "r": 295.55679, "b": 642.79817, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "fore model training. In Table 3, we present the mAP scores for a", "bbox": {"l": 53.79800000000001, "t": 645.38255, "r": 294.04715, "b": 653.75717, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "Mask R-CNN R50 network on different label sets. Where a label", "bbox": {"l": 53.79800000000001, "t": 656.34155, "r": 294.04715, "b": 664.71617, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "is down-mapped, we show its corresponding label, otherwise it", "bbox": {"l": 53.79800000000001, "t": 667.30055, "r": 294.04712, "b": 675.67517, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "bbox": {"l": 53.466999, "t": 678.25955, "r": 294.25174, "b": 686.63417, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "different labels respectively. The set of 5 labels contains the same", "bbox": {"l": 53.79800000000001, "t": 689.21855, "r": 294.04639, "b": 697.59317, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "labels as PubLayNet. However, due to the different definition of", "bbox": {"l": 53.79800000000001, "t": 700.177551, "r": 294.04712, "b": 708.55217, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 559.80682, "b": 128.22321, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"id": 109, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "bbox": {"l": 317.659, "t": 86.87298999999996, "r": 558.20068, "b": 95.34625000000017, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "document-wise and page-wise split for different label sets.", "bbox": {"l": 317.95499, "t": 97.83196999999996, "r": 559.73401, "b": 106.30524000000003, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "Naive page-wise split will result in", "bbox": {"l": 317.95499, "t": 108.79094999999995, "r": 467.72089, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "GLYPH<tildelow>", "bbox": {"l": 471.90900000000005, "t": 107.12798999999995, "r": 477.53900000000004, "b": 115.19768999999997, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "10% point improve-", "bbox": {"l": 477.54001000000005, "t": 108.79094999999995, "r": 559.80682, "b": 117.26422000000014, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "ment.", "bbox": {"l": 317.95502, "t": 119.74993999999992, "r": 341.37524, "b": 128.22321, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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false}, {"bbox": {"l": 504.6324200000001, "t": 296.36255, "r": 512.97119, "b": 304.73718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.40353, "r": 559.58496, "b": 416.49017, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"id": 171, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "bbox": {"l": 317.95499, "t": 331.40353, "r": 558.20233, "b": 339.77817, "coord_origin": "TOPLEFT"}}, {"id": 172, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "bbox": {"l": 317.95499, "t": 342.36255, "r": 558.2005, "b": 350.73718, "coord_origin": "TOPLEFT"}}, {"id": 173, "text": "assumption that the", "bbox": {"l": 317.95499, "t": 353.32153, "r": 393.16028, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 174, "text": "List", "bbox": {"l": 395.84201, "t": 353.36639, "r": 409.14905, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 175, "text": "is down-mapped to", "bbox": {"l": 412.33301, "t": 353.32153, "r": 485.02324999999996, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 176, "text": "Text", "bbox": {"l": 487.70401, "t": 353.36639, "r": 502.86761, "b": 361.70514, "coord_origin": "TOPLEFT"}}, {"id": 177, "text": "in PubLayNet.", "bbox": {"l": 506.05499, "t": 353.32153, "r": 559.58496, "b": 361.69617000000005, "coord_origin": "TOPLEFT"}}, {"id": 178, "text": "The results in Table 3 show that the prediction accuracy on the", "bbox": {"l": 317.686, "t": 364.28054999999995, "r": 558.2002, "b": 372.65518, "coord_origin": "TOPLEFT"}}, {"id": 179, "text": "remaining class labels does not change significantly when other", "bbox": {"l": 317.95499, "t": 375.23853, "r": 558.36877, "b": 383.61316, "coord_origin": "TOPLEFT"}}, {"id": 180, "text": "classes are merged into them. The overall macro-average improves", "bbox": {"l": 317.95499, "t": 386.19754, "r": 558.19958, "b": 394.57217, "coord_origin": "TOPLEFT"}}, {"id": 181, "text": "by around 5%, in particular when", "bbox": {"l": 317.95499, "t": 397.15652, "r": 439.49454, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 182, "text": "Page-footer", "bbox": {"l": 441.728, "t": 397.20139, "r": 481.8616, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 183, "text": "and", "bbox": {"l": 484.74298, "t": 397.15652, "r": 498.23743, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 184, "text": "Page-header", "bbox": {"l": 500.47299, "t": 397.20139, "r": 543.95105, "b": 405.54013, "coord_origin": "TOPLEFT"}}, {"id": 185, "text": "are", "bbox": {"l": 546.83197, "t": 397.15652, "r": 558.20142, "b": 405.53116000000006, "coord_origin": "TOPLEFT"}}, {"id": 186, "text": "excluded.", "bbox": {"l": 317.95496, "t": 408.11553999999995, "r": 352.37698, "b": 416.49017, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"id": 187, "text": "Impact of Document Split in Train and Test Set", "bbox": {"l": 317.95496, "t": 429.3949, "r": 549.8606, "b": 439.70398, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.62299, "t": 444.59653, "r": 559.71381, "b": 595.43718, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"id": 188, "text": "Many documents in DocLayNet have a unique styling. In order", "bbox": {"l": 317.95499, "t": 444.59653, "r": 558.36884, "b": 452.97116, "coord_origin": "TOPLEFT"}}, {"id": 189, "text": "to avoid overfitting on a particular style, we have split the train-,", "bbox": {"l": 317.95499, "t": 455.55554, "r": 559.19189, "b": 463.93018, "coord_origin": "TOPLEFT"}}, {"id": 190, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "bbox": {"l": 317.95499, "t": 466.51453, "r": 559.58185, "b": 474.88916, "coord_origin": "TOPLEFT"}}, {"id": 191, "text": "every document contributes pages to only one set. To the best of", "bbox": {"l": 317.95499, "t": 477.47354, "r": 558.20605, "b": 485.84818, "coord_origin": "TOPLEFT"}}, {"id": 192, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "bbox": {"l": 317.95499, "t": 488.43253, "r": 559.58203, "b": 496.80716, "coord_origin": "TOPLEFT"}}, {"id": 193, "text": "To quantify how this affects model performance, we trained and", "bbox": {"l": 317.686, "t": 499.39154, "r": 558.20032, "b": 507.76617, "coord_origin": "TOPLEFT"}}, {"id": 194, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "bbox": {"l": 317.95499, "t": 510.35052, "r": 559.5849, "b": 518.72516, "coord_origin": "TOPLEFT"}}, {"id": 195, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "bbox": {"l": 317.95499, "t": 521.30954, "r": 559.71381, "b": 529.68417, "coord_origin": "TOPLEFT"}}, {"id": 196, "text": "domised draw over the individual pages. As can be seen in Table 4,", "bbox": {"l": 317.95499, "t": 532.26855, "r": 559.18707, "b": 540.64317, "coord_origin": "TOPLEFT"}}, {"id": 197, "text": "the difference in model performance is surprisingly large: page-", "bbox": {"l": 317.95499, "t": 543.22655, "r": 559.71313, "b": 551.60117, "coord_origin": "TOPLEFT"}}, {"id": 198, "text": "wise splitting gains", "bbox": {"l": 317.62299, "t": 554.18555, "r": 388.35168, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 199, "text": "\u02dc", "bbox": {"l": 391.36499, "t": 552.50055, "r": 393.98318, "b": 560.87517, "coord_origin": "TOPLEFT"}}, {"id": 200, "text": "10% in mAP over the document-wise splitting.", "bbox": {"l": 390.59, "t": 554.18555, "r": 559.58191, "b": 562.56017, "coord_origin": "TOPLEFT"}}, {"id": 201, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "bbox": {"l": 317.686, "t": 565.14455, "r": 558.20032, "b": 573.51917, "coord_origin": "TOPLEFT"}}, {"id": 202, "text": "to accidental overestimation of model performance and should be", "bbox": {"l": 317.95499, "t": 576.10356, "r": 558.20508, "b": 584.4781800000001, "coord_origin": "TOPLEFT"}}, {"id": 203, "text": "avoided.", "bbox": {"l": 317.95499, "t": 587.06256, "r": 348.50354, "b": 595.43718, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"id": 204, "text": "Dataset Comparison", "bbox": {"l": 317.95499, "t": 608.34192, "r": 418.54776, "b": 618.65102, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.54355, "r": 559.18817, "b": 708.640137, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"id": 205, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of", "bbox": {"l": 317.686, "t": 623.54355, "r": 558.20575, "b": 631.91817, "coord_origin": "TOPLEFT"}}, {"id": 206, "text": "document layouts leads to more robust layout detection models. In", "bbox": {"l": 317.95499, "t": 634.50255, "r": 558.20624, "b": 642.87717, "coord_origin": "TOPLEFT"}}, {"id": 207, "text": "Table 5, we provide evidence for that. We trained models on each", "bbox": {"l": 317.686, "t": 645.46155, "r": 558.20319, "b": 653.83617, "coord_origin": "TOPLEFT"}}, {"id": 208, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "bbox": {"l": 317.95499, "t": 656.42055, "r": 558.74377, "b": 664.79517, "coord_origin": "TOPLEFT"}}, {"id": 209, "text": "and evaluated them on the test sets of the other datasets. Due to", "bbox": {"l": 317.95499, "t": 667.37955, "r": 558.20398, "b": 675.7541699999999, "coord_origin": "TOPLEFT"}}, {"id": 210, "text": "the different label sets and annotation styles, a direct comparison", "bbox": {"l": 317.95499, "t": 678.3385499999999, "r": 558.20062, "b": 686.71317, "coord_origin": "TOPLEFT"}}, {"id": 211, "text": "is not possible. Hence, we focussed on the common labels among", "bbox": {"l": 317.95499, "t": 689.29755, "r": 558.20343, "b": 697.672173, "coord_origin": "TOPLEFT"}}, {"id": 212, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "bbox": {"l": 317.95499, "t": 700.256554, "r": 531.07666, "b": 708.631172, "coord_origin": "TOPLEFT"}}, {"id": 213, "text": "Picture", "bbox": {"l": 533.16199, "t": 700.301384, "r": 557.2561, "b": 708.640137, "coord_origin": "TOPLEFT"}}, {"id": 214, "text": ",", "bbox": {"l": 557.255, "t": 700.256554, "r": 559.18817, "b": 708.631172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet\u2019s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 246.24382, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "bbox": {"l": 253.13897999999998, "t": 60.30902000000003, "r": 558.20288, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "bbox": {"l": 53.501999, "t": 86.87292000000002, "r": 294.32153, "b": 95.34618999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "bbox": {"l": 53.79800000000001, "t": 97.83191, "r": 295.64868, "b": 106.30517999999995, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "cLayNet data-sets. By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 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{"id": 30, "text": "42", "bbox": {"l": 256.49792, "t": 230.21155, "r": 264.83667, "b": 238.58618, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "total", "bbox": {"l": 154.629, "t": 241.16956000000005, "r": 171.2796, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "93", "bbox": {"l": 208.44701, "t": 241.16956000000005, "r": 216.78575000000004, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "34", "bbox": {"l": 232.1183, "t": 241.16956000000005, "r": 240.45705, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "30", "bbox": {"l": 256.49792, "t": 241.16956000000005, "r": 264.83667, "b": 249.54418999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "DocBank (DB)", "bbox": {"l": 78.530998, "t": 263.48650999999995, "r": 131.19963, "b": 271.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Figure", "bbox": {"l": 154.629, "t": 252.52752999999996, "r": 177.92371, "b": 260.90216, "coord_origin": 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"coord_origin": "TOPLEFT"}}, {"id": 44, "text": "total", "bbox": {"l": 154.629, "t": 274.44556, "r": 171.2796, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "48", "bbox": {"l": 208.44701, "t": 274.44556, "r": 216.78575000000004, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "68", "bbox": {"l": 232.1183, "t": 274.44556, "r": 240.45705, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "27", "bbox": {"l": 256.49792, "t": 274.44556, "r": 264.83667, "b": 282.82016, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "DocLayNet (DLN)", "bbox": {"l": 78.530998, "t": 307.72055, "r": 144.66716, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Figure", "bbox": {"l": 154.629, "t": 285.80255, "r": 177.92371, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "67", "bbox": {"l": 208.44701, "t": 285.80255, "r": 216.78575000000004, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "51", "bbox": {"l": 232.1183, "t": 285.80255, "r": 240.45705, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "72", "bbox": {"l": 256.49792, "t": 285.80255, "r": 264.83667, "b": 294.17719000000005, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Sec-header", "bbox": {"l": 154.629, "t": 296.76154, "r": 194.72675, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "53", "bbox": {"l": 208.44701, "t": 296.76154, "r": 216.78575000000004, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "-", "bbox": {"l": 234.77235, "t": 296.76154, "r": 237.80299000000002, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "68", "bbox": {"l": 256.49792, "t": 296.76154, "r": 264.83667, "b": 305.13617, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Table", "bbox": {"l": 154.629, "t": 307.72055, "r": 174.43578, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "87", "bbox": {"l": 208.44701, "t": 307.72055, "r": 216.78575000000004, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "43", "bbox": {"l": 232.1183, "t": 307.72055, "r": 240.45705, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "82", "bbox": {"l": 256.49792, "t": 307.72055, "r": 264.83667, "b": 316.09517999999997, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Text", "bbox": {"l": 154.629, "t": 318.67953, "r": 170.58919, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "77", "bbox": {"l": 208.44701, "t": 318.67953, "r": 216.78575000000004, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "-", "bbox": {"l": 234.77235, "t": 318.67953, "r": 237.80299000000002, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "84", "bbox": {"l": 256.49792, "t": 318.67953, "r": 264.83667, "b": 327.05417, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "total", "bbox": {"l": 154.629, "t": 329.63855, "r": 171.2796, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "59", "bbox": {"l": 208.44701, "t": 329.63855, "r": 216.78575000000004, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "47", "bbox": {"l": 232.1183, "t": 329.63855, "r": 240.45705, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "78", "bbox": {"l": 256.49792, "t": 329.63855, "r": 264.83667, "b": 338.01318, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Section-header", "bbox": {"l": 53.79800000000001, "t": 390.96539, "r": 106.2392, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": ",", "bbox": {"l": 106.239, "t": 390.92053, "r": 108.23331, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Table", "bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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By evaluating on common label classes of", "bbox": {"l": 53.79800000000001, "t": 108.79088999999999, "r": 294.0437, "b": 117.26415999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "each dataset, we observe that the DocLayNet-trained model", "bbox": {"l": 53.79800000000001, "t": 119.74987999999996, "r": 294.04373, "b": 128.22313999999994, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "has much less pronounced variations in performance across", "bbox": {"l": 53.79800000000001, "t": 130.70885999999996, "r": 294.0437, "b": 139.18213000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "all datasets.", "bbox": {"l": 53.79800000000001, "t": 141.66785000000004, "r": 101.15852, "b": 150.14111000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets."}, {"label": "table", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "table", "bbox": {"l": 72.6590347290039, "t": 172.48080444335938, "r": 274.83465576171875, "b": 339.8540344238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9892616271972656, "cells": [{"id": 8, "text": "Testing on", "bbox": {"l": 217.74099999999999, "t": 175.01855, "r": 256.26065, "b": 183.39319, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Training on", "bbox": {"l": 89.954002, "t": 185.97655999999995, "r": 133.24379, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "labels", "bbox": {"l": 154.629, "t": 185.97655999999995, "r": 175.47588, "b": 194.35119999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "PLN", "bbox": {"l": 204.69, "t": 185.97655999999995, "r": 220.54260000000002, "b": 194.35119999999995, 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"bbox": {"l": 110.482, "t": 390.96539, "r": 129.76332, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "and", "bbox": {"l": 132.21899, "t": 390.92053, "r": 145.86186, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Text", "bbox": {"l": 148.112, "t": 390.96539, "r": 163.14182, "b": 399.30414, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": ". Before training, we either mapped", "bbox": {"l": 163.13901, "t": 390.92053, "r": 294.04709, "b": 399.2951699999999, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "or excluded DocLayNet\u2019s other labels as specified in table 3, and", "bbox": {"l": 53.79800000000001, "t": 401.87954999999994, "r": 294.04712, "b": 410.25418, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "also PubLayNet\u2019s", "bbox": {"l": 53.79800000000001, "t": 412.83853, "r": 117.12856, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "List", "bbox": {"l": 119.362, "t": 412.88339, "r": 132.4342, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "to", "bbox": {"l": 135.177, "t": 412.83853, "r": 142.54416, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Text", "bbox": {"l": 144.77901, "t": 412.88339, "r": 159.66605, "b": 421.22214, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ". Note that the different clustering of", "bbox": {"l": 159.66701, "t": 412.83853, "r": 294.04562, "b": 421.21317, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "bbox": {"l": 53.79800000000001, "t": 423.79755, "r": 294.04614, "b": 432.17217999999997, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "mAP score for", "bbox": {"l": 53.79800000000001, "t": 434.75653, "r": 106.2066, "b": 443.13116, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Text", "bbox": {"l": 108.448, "t": 434.80139, "r": 123.30533, "b": 443.14014, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ".", "bbox": {"l": 123.305, "t": 434.75653, "r": 125.27761000000001, "b": 443.13116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet\u2019s other labels as specified in table 3, and also PubLayNet\u2019s List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.466999, "t": 445.71453999999994, "r": 295.55908, "b": 585.59618, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"id": 85, "text": "For comparison of DocBank with DocLayNet, we trained only", "bbox": {"l": 63.76100199999999, "t": 445.71453999999994, "r": 294.27582, "b": 454.08917, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "on", "bbox": {"l": 53.79800000000001, "t": 456.67355, "r": 62.989277, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Picture", "bbox": {"l": 64.852997, "t": 456.71841, "r": 88.947144, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "and", "bbox": {"l": 91.019997, "t": 456.67355, "r": 104.24454, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Table", "bbox": {"l": 106.108, "t": 456.71841, "r": 124.78053, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "clusters of each dataset. We had to exclude", "bbox": {"l": 126.85500000000002, "t": 456.67355, "r": 277.63235, "b": 465.04819, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Text", "bbox": {"l": 279.49701, "t": 456.71841, "r": 294.0484, "b": 465.05716, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "because successive paragraphs are often grouped together into a", "bbox": {"l": 53.79800000000001, "t": 467.63254, "r": 294.04709, "b": 476.00717, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "single object in DocBank. This paragraph grouping is incompatible", "bbox": {"l": 53.79800000000001, "t": 478.59155, "r": 294.04532, "b": 486.96619, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "bbox": {"l": 53.466999, "t": 489.55054, "r": 294.04538, "b": 497.92517, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Table 5, DocLayNet trained models yield better performance com-", "bbox": {"l": 53.528999, "t": 500.50955, "r": 295.55908, "b": 508.88419, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "pared to the previous datasets. It is noteworthy that the models", "bbox": {"l": 53.79800000000001, "t": 511.46854, "r": 294.04712, "b": 519.84317, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "trained on PubLayNet and DocBank perform very well on their", "bbox": {"l": 53.79800000000001, "t": 522.42755, "r": 294.21179, "b": 530.80219, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "own test set, but have a much lower performance on the foreign", "bbox": {"l": 53.79800000000001, "t": 533.38654, "r": 294.04712, "b": 541.76117, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "datasets. While this also applies to DocLayNet, the difference is", "bbox": {"l": 53.79800000000001, "t": 544.34555, "r": 294.04715, "b": 552.72017, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "bbox": {"l": 53.79800000000001, "t": 555.3045500000001, "r": 294.04712, "b": 563.67917, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "models are overall more robust and will produce better results for", "bbox": {"l": 53.79800000000001, "t": 566.26256, "r": 294.21411, "b": 574.63718, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "challenging, unseen layouts.", "bbox": {"l": 53.79800000000001, "t": 577.22156, "r": 157.52132, "b": 585.59618, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"id": 103, "text": "Example Predictions", "bbox": {"l": 53.79800000000001, "t": 605.06091, "r": 156.00534, "b": 615.37001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.55844, "b": 705.350174, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"id": 104, "text": "To conclude this section, we illustrate the quality of layout predic-", "bbox": {"l": 53.528999, "t": 620.26355, "r": 295.5571, "b": 628.63817, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "tions one can expect from DocLayNet-trained models by providing", "bbox": {"l": 53.79800000000001, "t": 631.22255, "r": 294.04532, "b": 639.59717, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "a selection of examples without any further post-processing ap-", "bbox": {"l": 53.79800000000001, "t": 642.18155, "r": 295.55618, "b": 650.5561700000001, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "bbox": {"l": 53.79800000000001, "t": 653.14055, "r": 294.04541, "b": 661.51517, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "test-set of DocLayNet. Results look decent in general across docu-", "bbox": {"l": 53.79800000000001, "t": 664.09956, "r": 295.55844, "b": 672.47417, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "ment categories, however one can also observe mistakes such as", "bbox": {"l": 53.79800000000001, "t": 675.05756, "r": 294.04712, "b": 683.43217, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "overlapping clusters of different classes, or entirely missing boxes", "bbox": {"l": 53.79800000000001, "t": 686.01656, "r": 294.04535, "b": 694.391174, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "due to low confidence.", "bbox": {"l": 53.79800000000001, "t": 696.975555, "r": 136.07368, "b": 705.350174, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"id": 112, "text": "6", "bbox": {"l": 317.95502, "t": 85.85986000000003, "r": 323.56229, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "CONCLUSION", "bbox": {"l": 334.47137, "t": 85.85986000000003, "r": 405.72961, "b": 96.16900999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 559.71375, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"id": 114, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "bbox": {"l": 317.95499, "t": 101.06151999999997, "r": 558.2038, "b": 109.43615999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "document conversion and layout analysis research community a", "bbox": {"l": 317.95499, "t": 112.02057000000002, "r": 558.2041, "b": 120.39520000000005, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "new and challenging dataset to improve and fine-tune novel ML", "bbox": {"l": 317.95499, "t": 122.97955000000002, "r": 558.43274, "b": 131.35419000000002, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "methods on. In contrast to many other datasets, DocLayNet was", "bbox": {"l": 317.95499, "t": 133.93854, "r": 558.20416, "b": 142.31317, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "created by human annotation in order to obtain reliable layout", "bbox": {"l": 317.95499, "t": 144.89752, "r": 558.20422, "b": 153.27215999999999, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "ground-truth on a wide variety of publication- and typesetting-", "bbox": {"l": 317.95499, "t": 155.85657000000003, "r": 559.71313, "b": 164.23119999999994, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "styles. Including a large proportion of documents outside the scien-", "bbox": {"l": 317.95499, "t": 166.81555000000003, "r": 559.71375, "b": 175.19019000000003, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "tific publishing domain adds significant value in this respect.", "bbox": {"l": 317.95499, "t": 177.77454, "r": 540.04382, "b": 186.14917000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 188.73352, "r": 559.71704, "b": 284.77917, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"id": 122, "text": "From the dataset, we have derived on the one hand reference", "bbox": {"l": 327.918, "t": 188.73352, "r": 558.19836, "b": 197.10815000000002, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "metrics for human performance on document-layout annotation", "bbox": {"l": 317.95499, "t": 199.69257000000005, "r": 558.20404, "b": 208.06719999999996, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "(through double and triple annotations) and on the other hand eval-", "bbox": {"l": 317.686, "t": 210.65155000000004, "r": 559.71704, "b": 219.02617999999995, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "uated the baseline performance of commonly used object detection", "bbox": {"l": 317.95499, "t": 221.60956, "r": 558.20245, "b": 229.98419, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "methods. We also illustrated the impact of various dataset-related", "bbox": {"l": 317.95499, "t": 232.56853999999998, "r": 558.20502, "b": 240.94317999999998, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "aspects on model performance through data-ablation experiments,", "bbox": {"l": 317.95499, "t": 243.52752999999996, "r": 559.18408, "b": 251.90215999999998, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "both from a size and class-label perspective. Last but not least, we", "bbox": {"l": 317.95499, "t": 254.48650999999995, "r": 558.20227, "b": 262.86114999999995, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "compared the accuracy of models trained on other public datasets", "bbox": {"l": 317.95499, "t": 265.44556, "r": 558.20349, "b": 273.82019, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "and showed that DocLayNet trained models are more robust.", "bbox": {"l": 317.95499, "t": 276.40454, "r": 540.99426, "b": 284.77917, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.62299, "t": 287.36353, "r": 558.43469, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"id": 131, "text": "To date, there is still a significant gap between human and ML", "bbox": {"l": 327.918, "t": 287.36353, "r": 558.43469, "b": 295.73816, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "accuracy on the layout interpretation task, and we hope that this", "bbox": {"l": 317.95499, "t": 298.32254, "r": 558.20013, "b": 306.69717, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "work will inspire the research community to close that gap.", "bbox": {"l": 317.62299, "t": 309.28152, "r": 535.65015, "b": 317.65616000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"id": 134, "text": "REFERENCES", "bbox": {"l": 317.95499, "t": 335.09189, "r": 387.3696, "b": 345.40097, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 348.70233, "r": 558.20099, "b": 371.16286999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"id": 135, "text": "[1]", "bbox": {"l": 321.198, "t": 348.70233, "r": 329.72415, "b": 355.21588, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021."}, {"label": "list_item", "id": 14, "page_no": 8, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 53.797997, "t": 670.52837, "r": 295.22174, "b": 708.92189, "coord_origin": "TOPLEFT"}, "confidence": 0.7421631217002869, "cells": [{"id": 55, "text": "[19]", "bbox": {"l": 53.797997, "t": 670.52837, "r": 65.760811, "b": 677.04189, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou.", "bbox": {"l": 67.582199, "t": 670.52837, "r": 295.11609, "b": 677.04189, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Layoutlm: Pre-training of text and layout for document image understanding.", "bbox": {"l": 69.234001, "t": 678.49837, "r": 295.11606, "b": 685.01189, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "In", "bbox": {"l": 69.234001, "t": 686.46837, "r": 75.155228, "b": 692.981895, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Proceedings of the 26th ACM SIGKDD International Conference on Knowledge", "bbox": {"l": 76.891998, "t": 686.50323, "r": 294.04382, "b": 692.988869, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Discovery and Data Mining", "bbox": {"l": 69.234001, "t": 694.473236, "r": 144.91022, "b": 700.95887, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": ", KDD, pages 1192-1200, New York, USA, 2020. Asso-", "bbox": {"l": 144.908, "t": 694.4383700000001, "r": 295.22174, "b": 700.951897, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ciation for Computing Machinery.", "bbox": {"l": 69.234001, "t": 702.408363, "r": 166.37207, "b": 708.92189, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery."}, {"label": "list_item", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 317.95499, "t": 527.06638, "r": 559.02637, "b": 541.5499, "coord_origin": "TOPLEFT"}, "confidence": 0.9088895320892334, "cells": [{"id": 63, "text": "[20]", "bbox": {"l": 317.95499, "t": 527.06638, "r": 329.24017, "b": 533.57993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout:", "bbox": {"l": 330.95837, "t": 527.06638, "r": 559.02637, "b": 533.57993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Fusion of visual and text features for document layout analysis, 2021.", "bbox": {"l": 333.39099, "t": 535.03638, "r": 530.03815, "b": 541.5499, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021."}, {"label": "list_item", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 317.95499, "t": 543.00638, "r": 558.9715, "b": 565.4599000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9247174859046936, "cells": [{"id": 66, "text": "[21]", "bbox": {"l": 317.95499, "t": 543.00638, "r": 329.54419, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei", "bbox": {"l": 331.30869, "t": 543.00638, "r": 558.20135, "b": 549.5199, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "Wu. Vsr: A unified framework for document layout analysis combining vision,", "bbox": {"l": 333.05701, "t": 550.97638, "r": 558.9715, "b": 557.4899, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "semantics and relations, 2021.", "bbox": {"l": 333.39099, "t": 558.94638, "r": 418.05988, "b": 565.4599000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021."}, {"label": "list_item", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 317.95499, "t": 566.91635, "r": 559.27539, "b": 597.3478700000001, "coord_origin": "TOPLEFT"}, "confidence": 0.8956930637359619, "cells": [{"id": 70, "text": "[22]", "bbox": {"l": 317.95499, "t": 566.91638, "r": 330.14108, "b": 573.4299, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas.", "bbox": {"l": 331.99646, "t": 566.91638, "r": 531.54553, "b": 573.4299, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "Corpus", "bbox": {"l": 537.12946, "t": 566.91635, "r": 558.19897, "b": 573.42989, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "conversion service: A machine learning platform to ingest documents at scale.", "bbox": {"l": 333.39099, "t": 574.88635, "r": 559.27539, "b": 581.39989, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "In", "bbox": {"l": 333.39099, "t": 582.85736, "r": 339.31223, "b": 589.3709, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "Proceedings of the 24th ACM SIGKDD International Conference on Knowledge", "bbox": {"l": 341.04901, "t": 582.89224, "r": 558.20081, "b": 589.37787, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Discovery and Data Mining", "bbox": {"l": 333.39099, "t": 590.8622399999999, "r": 409.56577, "b": 597.3478700000001, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": ", KDD, pages 774-782. ACM, 2018.", "bbox": {"l": 409.56598, "t": 590.82736, "r": 507.31765999999993, "b": 597.3409, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018."}, {"label": "list_item", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 317.95499, "t": 598.79736, "r": 559.3783, "b": 613.28787, "coord_origin": "TOPLEFT"}, "confidence": 0.8972970843315125, "cells": [{"id": 78, "text": "[23]", "bbox": {"l": 317.95499, "t": 598.79736, "r": 329.37976, "b": 605.3109, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmenta-", "bbox": {"l": 331.11923, "t": 598.79736, "r": 559.3783, "b": 605.3109, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "tion for deep learning.", "bbox": {"l": 333.39099, "t": 606.76736, "r": 396.9223, "b": 613.2809, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Journal of Big Data", "bbox": {"l": 399.43298, "t": 606.80225, "r": 453.66122, "b": 613.28787, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": ", 6(1):60, 2019.", "bbox": {"l": 453.66199, "t": 606.76736, "r": 493.49631, "b": 613.2809, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "[23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019."}], "headers": [{"label": "page_header", "id": 13, "page_no": 8, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8021690249443054, "cells": [{"id": 0, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "bbox": {"l": 53.79800000000001, "t": 60.30902000000003, "r": 347.01724, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}, "confidence": 0.8429245352745056, "cells": [{"id": 1, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA", "bbox": {"l": 365.75702, "t": 60.30902000000003, "r": 558.20282, "b": 68.57605000000012, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "KDD \u201922, August 14-18, 2022, Washington, DC, USA"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
index 9df735a7..8108fb67 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json
@@ -1 +1 @@
-{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.47799682617188, "t": 700.5064697265625, "r": 447.5447692871094, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.9844055175781, "t": 700.5064697265625, "r": 480.5931396484375, "b": 689.2177734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 675.5369873046875, "r": 480.5966491699219, "b": 639.093017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.66741943359375, "t": 454.45458984375, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, "b": 441.2538146972656, 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 625.2948608398438, "r": 318.4514465332031, "b": 612.7918090820312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 608.8849487304688, "r": 480.5956726074219, "b": 536.5759887695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 519.2052612304688, "r": 480.5989074707031, "b": 464.017822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 286.3288879394531, "r": 264.4082946777344, "b": 273.8258056640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 269.9199523925781, "r": 480.72003173828125, "b": 173.6999969482422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 174.2779541015625, "r": 480.59857177734375, "b": 125.87999725341797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.6674041748047, "t": 454.4546203613281, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 160.3699951171875, "t": 452.5425109863281, "r": 168.04522705078125, "b": 441.2538146972656, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "#", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 207.9739990234375, "t": 452.5425109863281, "r": 215.64923095703125, "b": 441.2538146972656, 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\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
index b6fb5969..e4596cb8 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 344.93649, "r": 278.3338, "b": 356.22519000000005, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 339.45749, "r": 348.26419, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 339.45749, "r": 417.12595, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 350.41647, "r": 418.46921, "b": 361.70517, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 339.45749, "r": 467.14142000000004, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 31, 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389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "0.955", "bbox": {"l": 363.67599, "t": 378.71048, "r": 384.66632, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "0.857", "bbox": {"l": 396.20599, "t": 378.71048, "r": 417.19632, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "5.39", "bbox": {"l": 440.767, "t": 378.71048, "r": 457.15039, "b": 389.99917999999997, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4", "bbox": {"l": 161.90601, "t": 397.53949, "r": 166.51474, "b": 408.82819, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4", "bbox": {"l": 209.509, "t": 397.53949, "r": 214.11774, "b": 408.82819, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 392.06049, "r": 271.41064, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "0.938", "bbox": {"l": 289.017, "t": 392.06049, "r": 310.00732, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.904", "bbox": {"l": 326.71701, "t": 392.06049, "r": 347.70734, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "0.927", "bbox": {"l": 363.67599, "t": 392.06049, "r": 384.66632, "b": 403.34918, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "0.853", "bbox": {"l": 394.61801, "t": 391.99771, "r": 418.77798, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "1.97", "bbox": {"l": 439.52701, "t": 391.99771, "r": 458.38336, "b": 403.40298, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 405.01147, "r": 272.94495, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "0.952", "bbox": {"l": 289.017, "t": 405.01147, "r": 310.00732, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "0.909", "bbox": {"l": 326.71701, "t": 405.01147, "r": 347.70734, "b": 416.30017, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "0.938", "bbox": {"l": 362.08801, "t": 404.9486999999999, "r": 386.24799, "b": 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"bbox": {"l": 363.67599, "t": 418.3614799999999, "r": 384.66632, "b": 429.65018, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 418.29871, "r": 418.77798, "b": 429.70398, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 418.29871, "r": 458.38336, "b": 429.70398, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 431.31246999999996, "r": 272.94495, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 431.31246999999996, "r": 310.00732, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 431.31246999999996, "r": 347.70734, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 431.24969, "r": 386.24799, "b": 442.65497, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 431.31246999999996, "r": 417.19632, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 431.31246999999996, "r": 457.15039, "b": 442.60117, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 450.14248999999995, "r": 166.51474, "b": 461.43118, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 450.14248999999995, "r": 214.11774, "b": 461.43118, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 444.66248, "r": 271.41064, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 444.66248, "r": 310.00732, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 444.66248, "r": 345.40439, "b": 455.95117, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 444.5996999999999, "r": 386.24799, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "9"}]}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 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{"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 456.00497, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 457.61447, "r": 272.94495, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 457.61447, "r": 310.00732, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 457.61447, "r": 347.70734, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 457.61447, "r": 384.66632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 457.61447, "r": 417.19632, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 457.61447, "r": 451.26175, "b": 468.90317, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373533129692078, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 91.49352999999996, "r": 447.54476999999997, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858679533004761, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98441, "t": 91.49352999999996, "r": 480.59314, "b": 102.78223000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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"end_row_offset_idx": 7, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.61801, "t": 444.5996999999999, "r": 418.77798, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.52701, "t": 444.5996999999999, "r": 458.38336, "b": 468.90317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}, "confidence": 0.9589295387268066, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 505.67111, "r": 149.40306, "b": 518.17419, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85905, "t": 505.67111, "r": 264.40829, "b": 518.17419, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 522.08005, "r": 480.72003, "b": 618.3, "coord_origin": "TOPLEFT"}, "confidence": 0.9849976301193237, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 522.08005, "r": 479.72983, "b": 534.61301, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 534.03604, "r": 480.5897499999999, "b": 546.569, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 545.99104, "r": 480.72003, "b": 558.524, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 557.94604, "r": 480.60577, "b": 570.479, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 641.63203, "r": 480.59384000000006, "b": 654.16499, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 653.58704, "r": 405.7995, "b": 666.12, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59665, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9806435108184814, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 116.46301000000005, "r": 480.59067, "b": 128.99597000000006, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 128.41803000000004, "r": 480.59665, "b": 140.95099000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 140.37401999999997, "r": 210.78761, "b": 152.90697999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 166.70514000000003, "r": 149.40306, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85905, "t": 166.70514000000003, "r": 318.45145, "b": 179.20818999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 183.11505, "r": 480.59567, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9850425124168396, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 183.11505, "r": 479.74982000000006, "b": 195.64801, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 195.07007, "r": 480.58765, "b": 207.60303, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 207.02502000000004, "r": 480.58859000000007, "b": 219.55798000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 218.98004000000003, "r": 480.59567, "b": 231.51300000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 230.93506000000002, "r": 440.9425, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86800999999997, "t": 230.98486000000003, "r": 480.58792, "b": 243.46802000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.765, "t": 242.94086000000004, "r": 145.19585, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.149, "t": 242.89104999999995, "r": 311.22256, "b": 255.42400999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 480.59890999999993, "b": 327.98218, "coord_origin": "TOPLEFT"}, "confidence": 0.9591907262802124, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.765, "t": 272.79474000000005, "r": 159.22983, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34442, "t": 272.79474000000005, "r": 174.71301, "b": 284.1999799999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.133, "t": 272.85748, "r": 480.58101999999997, "b": 284.14618, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.765, "t": 283.81647, "r": 480.59890999999993, "b": 295.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.765, "t": 294.77547999999996, "r": 480.59887999999995, "b": 306.06418, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.765, "t": 305.73447, "r": 480.59180000000003, "b": 317.02316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.765, "t": 316.69348, "r": 480.58471999999995, "b": 327.98218, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.6674041748047, "t": 337.5453796386719, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 339.45749, "r": 168.04523, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 352.40848, "r": 183.82895, "b": 363.69717, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 339.45749, "r": 215.64923000000002, "b": 350.74619, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19501, "t": 352.40848, "r": 231.42303, "b": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 569.90103, "r": 480.5936899999999, "b": 582.43399, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 581.85603, "r": 480.59158, "b": 594.38899, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 593.81204, "r": 480.58080999999993, "b": 606.345, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 605.76704, "r": 206.79959, "b": 618.3, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 617.72205, "r": 480.59857000000005, "b": 666.12, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 617.72205, "r": 480.59479, "b": 630.255, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 629.6770300000001, "r": 480.59857000000005, "b": 642.2099900000001, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. 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diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.json b/tests/data/groundtruth/docling_v2/2305.03393v1.json
index 820bade4..35bac03b 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.json
@@ -1 +1 @@
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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76499938964844, "t": 676.1008911132812, "r": 480.59735107421875, "b": 645.4859008789062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 139.34305, "t": 622.30841, "r": 476.01270000000005, "b": 591.81409, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 222]}], "orig": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]", "text": "Maksym Lysak [0000 \u2212 0002 \u2212 3723 \u2212 $^{6960]}$, Ahmed Nassar[0000 \u2212 0002 \u2212 9468 \u2212 $^{0822]}$, Nikolaos Livathinos [0000 \u2212 0001 \u2212 8513 \u2212 $^{3491]}$, Christoph Auer[0000 \u2212 0001 \u2212 5761 \u2212 $^{0422]}$, [0000 \u2212 0002 \u2212 8088 \u2212 0823]"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 229.52109000000002, "t": 596.41626, "r": 298.6087, "b": 587.61926, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "and Peter Staar", "text": "and Peter Staar"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 279.1051, "t": 574.79602, "r": 336.25153, "b": 566.72632, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.96609, "t": 563.19147, "r": 392.38983, "b": 555.72247, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 396.41107, "t": 511.01648, "r": 402.97336, "b": 502.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 418.58682, "t": 511.10208, "r": 425.14911, "b": 502.57657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 395.74835, "t": 488.76273, 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 164.74348, "t": 533.39905, "r": 185.21857, "b": 526.29443, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 164.3548, "t": 525.50293, "r": 222.05352999999997, "b": 518.39832, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "sequence length:", "text": "sequence length:"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 224.15326, "t": 525.50293, "r": 232.57729, "b": 518.39832, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 339.68621826171875, "r": 480.5923156738281, "b": 271.1133117675781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 374.49326, "t": 623.40637, "r": 381.66843, "b": 614.08459, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 398.74011, "t": 623.49994, "r": 405.91528, "b": 614.17816, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 373.76862, "t": 599.07446, "r": 380.94379, "b": 589.75269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], 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cells: \"C\"", "text": "- simple cells: \"C\""}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 539.06744, "r": 337.33313, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.15491, "t": 539.06744, "r": 421.98624, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "- horizontal merges: \"C\", \"L\"", "text": "- horizontal merges: \"C\", \"L\""}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 529.12952, "r": 337.29868, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.086, "t": 529.12952, "r": 415.34375, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "- vertical merges: \"C\", \"U\"", "text": "- vertical merges: \"C\", \"U\""}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 519.19159, "r": 426.59875, "b": 512.97711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "text": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 185.67178, "t": 547.95776, "r": 189.35544, "b": 541.74329, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/217", "parent": {"cref": 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7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 585.16132, "r": 175.72659, "b": 577.39319, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 274.29419, "t": 623.72028, "r": 280.2735, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 359.56152, "t": 623.72028, "r": 365.54083, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 548.78851, "r": 175.27112, "b": 541.02039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "E", "text": "E"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 486.7041931152344, "r": 246.6519775390625, "b": 477.8972473144531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "level": 1}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 466.7522277832031, "r": 363.7961730957031, "b": 457.95526123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 444.8291931152344, "r": 480.5890197753906, "b": 424.0662536621094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 147.30025, "t": 251.26836000000003, "r": 149.70605, "b": 248.89992000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1."}, {"self_ref": "#/texts/249", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 150.90895, "t": 251.26836000000003, "r": 155.72055, "b": 248.89992000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Item", "text": "Item"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 162.75987, "t": 256.60619999999994, "r": 172.2963, "b": 254.23775999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Amount", "text": "Amount"}, 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"text": "3"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 340.80972, "t": 237.40688, "r": 343.33673, "b": 233.14365999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 330.97992, "t": 237.16965000000005, "r": 333.50693, "b": 232.90643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/335", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. 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"label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.3756103515625, "t": 698.22900390625, "r": 480.5894775390625, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58837890625, "b": 614.2323608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 390]}], "orig": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"self_ref": "#/texts/455", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 298.1791687011719, "r": 197.68641662597656, "b": 287.61077880859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/456", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.37100219726562, "t": 269.1201477050781, "r": 480.5920104980469, "b": 228.12855529785156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. 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"charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/groups/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.96609, "t": 563.19147, "r": 392.38983, "b": 555.72247, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 521.6988525390625, "r": 452.248779296875, "b": 327.2655334472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.11109924316406, "t": 313.3060607910156, "r": 452.2415771484375, "b": 294.2145080566406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 269.88031005859375, "r": 228.933837890625, "b": 259.3119201660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "level": 1}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 243.7134552001953, "r": 480.595947265625, "b": 163.18548583984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.76512145996094, "t": 159.85244750976562, "r": 480.5958251953125, "b": 127.14546966552734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 665.6658325195312, "r": 480.5918884277344, "b": 591.7794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 396.41107, "t": 511.01648, "r": 402.97336, "b": 502.49097, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 418.58682, "t": 511.10208, "r": 425.14911, "b": 502.57657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 395.74835, "t": 488.76273, 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"#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.76499938964844, "t": 339.68621826171875, "r": 480.5923156738281, "b": 271.1133117675781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.7650146484375, "t": 267.44927978515625, "r": 480.5948181152344, "b": 127.14530181884766, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 673.0662231445312, "r": 480.5918273925781, "b": 580.5831298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 577.1641235351562, "r": 480.5957336425781, "b": 460.7701416015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 457.35211181640625, "r": 480.5956726074219, "b": 352.9132385253906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 329.91204833984375, "r": 236.76913452148438, "b": 319.3436584472656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "level": 1}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.76498413085938, "t": 303.3141784667969, "r": 484.1204833984375, "b": 127.14423370361328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59576416015625, "b": 532.7620849609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 529.3430786132812, "r": 480.595703125, "b": 305.3533020019531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 301.93426513671875, "r": 480.5937805175781, "b": 209.4513397216797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 186.45016479492188, "r": 269.6244201660156, "b": 175.88177490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "level": 1}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.76498413085938, "t": 159.85231018066406, "r": 480.59368896484375, "b": 127.14434051513672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5937805175781, "b": 604.4931640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.6070098876953, "t": 570.9207153320312, "r": 469.7522277832031, "b": 562.7882080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"self_ref": "#/texts/140", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.76499938964844, "t": 423.793212890625, "r": 480.5947570800781, "b": 259.57940673828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"self_ref": "#/texts/141", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 255.95736694335938, "r": 480.5928955078125, "b": 211.29440307617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"self_ref": "#/texts/142", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.7650146484375, "t": 207.67337036132812, "r": 480.5947265625, "b": 127.14539337158203, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.59478759765625, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 648.5172119140625, "r": 480.595703125, "b": 496.2580871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 471.368896484375, "r": 372.50848388671875, "b": 460.8005065917969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "level": 1}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 442.8830261230469, "r": 480.5947265625, "b": 350.400146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 326.1280822753906, "r": 261.80108642578125, "b": 317.3211364746094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "level": 1}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76498413085938, "t": 303.0021057128906, "r": 480.5887145996094, "b": 270.2941589355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.708984375, "t": 266.4981384277344, "r": 409.3113708496094, "b": 257.701171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99298095703125, "t": 244.0301055908203, "r": 460.54443359375, "b": 235.22317504882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.99301147460938, "t": 231.43710327148438, "r": 480.59393310546875, "b": 210.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 206.8881072998047, "r": 480.58856201171875, "b": 186.1261749267578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 182.34010314941406, "r": 454.5549621582031, "b": 173.53317260742188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.9930419921875, "t": 169.74610900878906, "r": 328.61676025390625, "b": 160.93917846679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.76504516601562, "t": 147.8971405029297, "r": 480.5928039550781, "b": 127.14515686035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58740234375, "b": 636.1503295898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 374.49326, "t": 623.40637, "r": 381.66843, "b": 614.08459, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 398.74011, "t": 623.49994, "r": 405.91528, "b": 614.17816, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 373.76862, "t": 599.07446, "r": 380.94379, "b": 589.75269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], 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cells: \"C\"", "text": "- simple cells: \"C\""}, {"self_ref": "#/texts/211", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 539.06744, "r": 337.33313, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.15491, "t": 539.06744, "r": 421.98624, "b": 532.85297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "- horizontal merges: \"C\", \"L\"", "text": "- horizontal merges: \"C\", \"L\""}, {"self_ref": "#/texts/213", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 529.12952, "r": 337.29868, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 340.086, "t": 529.12952, "r": 415.34375, "b": 522.91504, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "- vertical merges: \"C\", \"U\"", "text": "- vertical merges: \"C\", \"U\""}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 334.51135, "t": 519.19159, "r": 426.59875, "b": 512.97711, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "text": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 185.67178, "t": 547.95776, "r": 189.35544, "b": 541.74329, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/217", "parent": {"cref": 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7, "bbox": {"l": 169.74728, "t": 624.11774, "r": 175.72659, "b": 616.34961, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 585.16132, "r": 175.72659, "b": 577.39319, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/226", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 274.29419, "t": 623.72028, "r": 280.2735, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 359.56152, "t": 623.72028, "r": 365.54083, "b": 615.95215, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/pictures/2"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.74728, "t": 548.78851, "r": 175.27112, "b": 541.02039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "E", "text": "E"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 486.7041931152344, "r": 246.6519775390625, "b": 477.8972473144531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "level": 1}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76499938964844, "t": 466.7522277832031, "r": 363.7961730957031, "b": 457.95526123046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 444.8291931152344, "r": 480.5890197753906, "b": 424.0662536621094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 420.9151916503906, "r": 480.59228515625, "b": 400.15325927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 397.002197265625, "r": 226.0736083984375, "b": 388.19525146484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "level": 1}, {"self_ref": "#/texts/234", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.70098876953125, "t": 385.0332336425781, "r": 480.5923767089844, "b": 352.3262939453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 349.17425537109375, "r": 474.5901794433594, "b": 340.3673095703125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 337.21624755859375, "r": 480.58746337890625, "b": 316.4543151855469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.97299194335938, "t": 313.3032531738281, "r": 480.5945739746094, "b": 292.5403137207031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 279.40728759765625, "r": 480.5958251953125, "b": 151.05833435058594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.76498413085938, "t": 147.89730834960938, "r": 480.5926513671875, "b": 127.14533233642578, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 139.37193298339844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.8133544921875, "t": 698.22900390625, "r": 231.72227478027344, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5888366699219, "b": 652.314208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 630.4431762695312, "r": 319.3470764160156, "b": 621.63623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "level": 1}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 609.7182006835938, "r": 480.59576416015625, "b": 493.32415771484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 470.83599853515625, "r": 229.03533935546875, "b": 460.2676086425781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "level": 1}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 444.7501525878906, "r": 480.59527587890625, "b": 340.3122863769531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.7650146484375, "t": 307.35186767578125, "r": 480.5908203125, "b": 288.2603454589844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. 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"text": "3"}, {"self_ref": "#/texts/318", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 340.80972, "t": 237.40688, "r": 343.33673, "b": 233.14365999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/319", "parent": {"cref": "#/pictures/3"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 330.97992, "t": 237.16965000000005, "r": 333.50693, "b": 232.90643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/320", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59173583984375, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}, {"self_ref": "#/texts/321", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.47799682617188, "t": 698.22900390625, "r": 447.54290771484375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/322", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.98431396484375, "t": 698.22900390625, "r": 480.59124755859375, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 673.0662231445312, "r": 480.5957946777344, "b": 640.3582153320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 622.8141479492188, "r": 318.44842529296875, "b": 614.0072021484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "level": 1}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 606.4141845703125, "r": 480.5927734375, "b": 537.8411254882812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.76498413085938, "t": 516.9276733398438, "r": 480.59539794921875, "b": 464.9591979980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 283.84820556640625, "r": 264.4033203125, "b": 275.041259765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "level": 1}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 267.44921875, "r": 480.59576416015625, "b": 174.9652557373047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.76499938964844, "t": 171.80722045898438, "r": 480.59576416015625, "b": 127.1452407836914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/332", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 676.163818359375, "r": 480.59356689453125, "b": 646.1133422851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8)."}, {"self_ref": "#/texts/333", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 503.085205078125, "r": 257.0867919921875, "b": 494.27825927734375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "level": 1}, {"self_ref": "#/texts/334", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 482.13922119140625, "r": 480.5898132324219, "b": 425.5223083496094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"self_ref": "#/texts/335", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.76499938964844, "t": 394.4098815917969, "r": 480.591064453125, "b": 352.2828369140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 180.12473, "t": 275.7667799999999, "r": 190.62042, "b": 273.05008, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "<table>", "text": "<table>"}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/4"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 183.2438, "t": 271.86792, "r": 304.54797, "b": 269.15121, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td 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"label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.3756103515625, "t": 698.22900390625, "r": 480.5894775390625, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.76499938964844, "t": 666.2008056640625, "r": 480.58837890625, "b": 614.2323608398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 390]}], "orig": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"self_ref": "#/texts/438", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.5049, "t": 479.54968, "r": 177.59613, "b": 471.63614, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B"}, {"self_ref": "#/texts/439", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.05823, "t": 299.34726, "r": 177.14946, "b": 291.43372, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C"}, {"self_ref": "#/texts/440", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.51833999999997, "r": 374.96332, "b": 158.58319000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence"}, {"self_ref": "#/texts/441", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.64224000000002, "r": 398.05978, "b": 168.70709, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present"}, {"self_ref": "#/texts/442", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.64209, "t": 326.40216, "r": 437.50800000000004, "b": 320.46701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions"}, {"self_ref": "#/texts/443", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 503.64037999999994, "r": 239.23492, "b": 497.7052299999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of"}, {"self_ref": "#/texts/444", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.89114, "t": 497.10577, "r": 251.52917, "b": 491.17062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells"}, {"self_ref": "#/texts/445", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.83432, "t": 607.24011, "r": 253.61339, "b": 597.18365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A"}, {"self_ref": "#/texts/446", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.18976, "t": 184.19390999999996, "r": 381.54663, "b": 178.25875999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end"}, {"self_ref": "#/texts/447", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27777, "t": 410.63712, "r": 180.18666, "b": 388.59933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL"}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/5"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.27747, "t": 236.22305000000006, "r": 180.18663, "b": 213.25220000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML"}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 698.22900390625, "r": 143.97886657714844, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.82052612304688, "t": 698.22900390625, "r": 231.72048950195312, "b": 690.1593017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 674.4510498046875, "r": 219.25479125976562, "b": 663.8826293945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "level": 1}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 645.13623046875, "r": 480.595703125, "b": 588.5181884765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 584.5562133789062, "r": 480.59478759765625, "b": 468.1632080078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.76499938964844, "t": 464.201171875, "r": 480.5948181152344, "b": 323.8973388671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. 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diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.md b/tests/data/groundtruth/docling_v2/2305.03393v1.md
index 362c0077..b5838fa9 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.md
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.md
@@ -38,7 +38,7 @@ Approaches to formalize the logical structure and layout of tables in electronic
 
 Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.
 
-Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.
+Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( &lt;td&gt; ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.
 
 Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.
 
@@ -46,13 +46,13 @@ Im2Seq approaches have shown to be well-suited for the TSR task and allow a full
 
 All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-
 
-ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.
+ulary and can be interpreted as a table structure. For example, with the HTML tokens &lt;table&gt; , &lt;/table&gt; , &lt;tr&gt; , &lt;/tr&gt; , &lt;td&gt; and &lt;/td&gt; , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.
 
 Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.
 
 <!-- image -->
 
-Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.
+Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( &lt;td&gt; and &lt;/td&gt; ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.
 
 Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.
 
@@ -194,7 +194,7 @@ Secondly, OTSL has more inherent structure and a significantly restricted vocabu
 - 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)
 - 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226
 - 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)
-- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834
+- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834
 - 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397
 - 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)
 
diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
index fe3614c1..a4737d71 100644
--- a/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
+++ b/tests/data/groundtruth/docling_v2/2305.03393v1.pages.json
@@ -1 +1 @@
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"coord_origin": "TOPLEFT"}}, {"id": 39, "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "bbox": {"l": 222.96609, "t": 228.80853000000002, "r": 392.38983, "b": 236.27752999999996, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.969645619392395, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899651169776917, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.89910999999995, "r": 480.59735, "b": 146.51409999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8918352127075195, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure", "bbox": {"l": 134.765, "t": 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.969645619392395, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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{"id": 1, "label": "text", "bbox": {"l": 163.1111, "t": 270.30115, "r": 452.24878000000007, "b": 464.73447, "coord_origin": "TOPLEFT"}, "confidence": 0.9807655215263367, "cells": [{"id": 40, "text": "Abstract.", "bbox": {"l": 163.1111, "t": 270.30115, "r": 206.6358, "b": 278.22748, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Extracting tables from documents is a crucial task in any", "bbox": {"l": 211.6171, "t": 270.36395000000005, "r": 452.2447199999999, "b": 278.43364999999994, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "document conversion pipeline. Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304056167602539, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.969645619392395, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899651169776917, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", 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"text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, 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452.31378, "r": 480.59232000000003, "b": 520.88669, "coord_origin": "TOPLEFT"}, "confidence": 0.9505022168159485, "cells": [{"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, "coord_origin": "TOPLEFT"}, "confidence": 0.968888521194458, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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"coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8938870429992676, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37413024902344, "r": 464.3611145019531, "b": 425.8467712402344, "coord_origin": "TOPLEFT"}, "confidence": 0.968888521194458, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845514893531799, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580383658409119, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076677560806274, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861934661865, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439420104026794, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325792193412781, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467446208000183, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.9290876388549805, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853582382202148, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692808389663696, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890058755874634, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.904558539390564, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472552299499512, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.9783945083618164, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41453552246094, "t": 233.51222229003906, "r": 476.5608215332031, "b": 340.23046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269196391105652, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696115612983704, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919057250022888, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919057250022888, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633576273918152, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856825470924377, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.9569860100746155, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879342317581177, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559569358825684, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671323299407959, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8556628227233887, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184004068374634, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319751858711243, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438934922218323, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9035711288452148, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636069297790527, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443687796592712, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378579020500183, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919057250022888, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, 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Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "picture", "bbox": {"l": 164.65020751953125, "t": 163.79708862304688, "r": 449.5506591796875, "b": 280.3410949707031, "coord_origin": "TOPLEFT"}, "confidence": 0.7868280410766602, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 202.39239999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "C", "bbox": {"l": 386.68707, "t": 205.13756999999998, "r": 393.86224, "b": 214.45934999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "C", "bbox": {"l": 398.65729, "t": 180.73279000000002, "r": 405.83246, "b": 190.05457, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 410.77908, "t": 180.73859000000004, "r": 417.95425, "b": 190.06035999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 422.90503, "t": 180.65247, "r": 430.08020000000005, "b": 189.97424, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 398.7807, "t": 192.98865, "r": 405.95587, "b": 202.31042000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 410.90164, "t": 192.99487, "r": 418.07681, "b": 202.31664999999998, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 423.02753, "t": 192.909, "r": 430.2027, "b": 202.23077, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 398.78235, "t": 205.31573000000003, "r": 405.95752, "b": 214.63751000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 410.90414, "t": 205.32196, "r": 418.07932, "b": 214.64373999999998, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 423.03003, "t": 205.23614999999995, "r": 430.20520000000005, "b": 214.55791999999997, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 386.50574, "t": 217.03882, "r": 393.68091, "b": 226.36059999999998, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 398.60181, "t": 217.21704, "r": 405.77698, "b": 226.53882, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 410.72275, "t": 217.22321, "r": 417.89792, "b": 226.54498, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 422.84869, "t": 217.13738999999998, "r": 430.02386, "b": 226.45916999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "NL", "bbox": {"l": 435.16009999999994, "t": 167.69011999999998, "r": 447.86273, "b": 177.01189999999997, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "NL", "bbox": {"l": 435.44415, "t": 180.20025999999996, "r": 448.14679, "b": 189.52202999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "NL", "bbox": {"l": 435.46735, "t": 192.49474999999995, "r": 448.16998000000007, "b": 201.81652999999994, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "NL", "bbox": {"l": 435.38202, "t": 204.83025999999995, "r": 448.08466, "b": 214.15204000000006, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "NL", "bbox": {"l": 435.59906, "t": 217.2337, "r": 448.3017, "b": 226.55548, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "U", "bbox": {"l": 374.14957, "t": 205.23492, "r": 381.32474, "b": 214.55669999999998, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "U", "bbox": {"l": 374.0419, "t": 217.14648, "r": 381.21707, "b": 226.46826, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "U", "bbox": {"l": 374.34418, "t": 180.93488000000002, "r": 381.51935, "b": 190.25665000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "L", "bbox": {"l": 387.76285, "t": 168.57788000000005, "r": 393.28833, "b": 177.89966000000004, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", 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"coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": 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First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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"r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 84, "label": "text", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 85, "label": "text", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169163107872009, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588111639022827, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585384726524353, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506180167198181, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247209548950195, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785565137863159, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333061575889587, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65020751953125, "t": 163.79708862304688, "r": 449.5506591796875, "b": 280.3410949707031, "coord_origin": "TOPLEFT"}, "confidence": 0.7868280410766602, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 202.39239999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "C", "bbox": {"l": 386.68707, "t": 205.13756999999998, "r": 393.86224, "b": 214.45934999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "C", "bbox": {"l": 398.65729, "t": 180.73279000000002, "r": 405.83246, "b": 190.05457, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 410.77908, "t": 180.73859000000004, "r": 417.95425, "b": 190.06035999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 422.90503, "t": 180.65247, "r": 430.08020000000005, "b": 189.97424, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 398.7807, "t": 192.98865, "r": 405.95587, "b": 202.31042000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 410.90164, "t": 192.99487, "r": 418.07681, "b": 202.31664999999998, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 423.02753, "t": 192.909, "r": 430.2027, "b": 202.23077, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 398.78235, "t": 205.31573000000003, "r": 405.95752, "b": 214.63751000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 410.90414, "t": 205.32196, "r": 418.07932, "b": 214.64373999999998, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 423.03003, "t": 205.23614999999995, "r": 430.20520000000005, "b": 214.55791999999997, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 386.50574, "t": 217.03882, "r": 393.68091, "b": 226.36059999999998, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 398.60181, "t": 217.21704, "r": 405.77698, "b": 226.53882, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 410.72275, "t": 217.22321, "r": 417.89792, "b": 226.54498, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 422.84869, "t": 217.13738999999998, "r": 430.02386, "b": 226.45916999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "NL", "bbox": 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246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233025312423706, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169163107872009, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588111639022827, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585384726524353, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506180167198181, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247209548950195, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259926080703735, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785565137863159, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590606093406677, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9326502680778503, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749731183052063, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 147.30025, "t": 553.15465, "r": 149.70605, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Item", "bbox": {"l": 150.90895, "t": 553.15465, "r": 155.72055, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Extracted", "bbox": {"l": 152.05046, "t": 517.0098, "r": 171.24945, "b": 521.27298, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Table Images", "bbox": {"l": 148.13347, "t": 522.3122900000001, "r": 175.16759, "b": 526.57547, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 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433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "BBoxes in sync", "bbox": {"l": 333.07819, "t": 541.82269, "r": 364.14691, "b": 546.08591, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "with tag sequence", "bbox": {"l": 333.07819, "t": 545.6102, "r": 369.71542, "b": 549.87341, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "Encoder", "bbox": {"l": 232.65881000000002, "t": 515.24139, "r": 249.58894000000004, "b": 519.50458, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "Structure", "bbox": {"l": 269.8219, "t": 545.97102, "r": 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"text": "C", "bbox": {"l": 340.27948, "t": 583.39737, "r": 344.1207, "b": 588.38777, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "C", "bbox": {"l": 346.76874, "t": 583.40068, "r": 350.60995, "b": 588.39108, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "C", "bbox": {"l": 333.56549, "t": 583.35474, "r": 337.40671, "b": 588.34514, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "NL", "bbox": {"l": 353.03326, "t": 556.8831299999999, "r": 359.83362, "b": 561.87354, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "NL", "bbox": {"l": 353.18604, "t": 563.58044, "r": 359.98639, "b": 568.57085, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "NL", "bbox": {"l": 353.19864, "t": 570.1623500000001, "r": 359.99899, "b": 575.15276, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "NL", "bbox": {"l": 353.1532, "t": 576.76611, "r": 359.95355, "b": 581.75652, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "NL", "bbox": {"l": 353.26935, "t": 583.40628, "r": 360.0697, "b": 588.3966800000001, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "L", "bbox": {"l": 347.37979, "t": 557.08235, "r": 350.33786, "b": 562.07275, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3", "bbox": {"l": 331.14026, "t": 564.2907700000001, "r": 333.66727, "b": 568.55399, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "2", "bbox": {"l": 340.80972, "t": 554.59312, "r": 343.33673, "b": 558.85634, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "1", "bbox": {"l": 330.97992, "t": 554.83035, "r": 333.50693, "b": 559.09357, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301319479942322, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.863182783126831, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 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prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301319479942322, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.863182783126831, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096405029297, "t": 508.06390380859375, "r": 472.7337951660156, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464740753173828, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549515843391418, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9515272378921509, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297954440116882, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.7096405029297, "t": 508.06390380859375, "r": 472.7337951660156, "b": 593.67724609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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"original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 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The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. 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Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. 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"text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.93733811378479, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.885762631893158, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.985123336315155, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579680562019348, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570783376693726, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747375488281, "r": 475.00372314453125, "b": 469.4720764160156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901033639907837, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.985123336315155, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.93733811378479, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.885762631893158, "cells": [{"id": 1, "text": "9", "bbox": {"l": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482715129852295, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.6731414794922, "t": 444.6224365234375, "r": 451.7003479003906, "b": 663.213623046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9742222428321838, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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"section_header", "bbox": {"l": 134.765, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9546456933021545, "cells": [{"id": 55, "text": "5.3", "bbox": {"l": 134.765, "t": 288.91479, "r": 149.40205, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Qualitative Results", "bbox": {"l": 160.85904, "t": 288.91479, "r": 257.08679, "b": 297.72173999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.3 Qualitative Results"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58981, "b": 366.47769, "coord_origin": "TOPLEFT"}, "confidence": 0.9834066033363342, "cells": [{"id": 57, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5", "bbox": {"l": 134.765, "t": 309.86078, "r": 480.58777, "b": 318.65775, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482715129852295, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.6731414794922, "t": 444.6224365234375, "r": 451.7003479003906, "b": 663.213623046875, "coord_origin": "TOPLEFT"}, "confidence": 0.9742222428321838, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39266967773438, "t": 181.9679718017578, "r": 447.3527526855469, "b": 634.0032958984375, "coord_origin": "TOPLEFT"}, "confidence": 0.7615764737129211, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39266967773438, "t": 181.9679718017578, "r": 447.3527526855469, "b": 634.0032958984375, "coord_origin": "TOPLEFT"}, "confidence": 0.7615764737129211, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": [{"id": 3, "label": "text", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 12, "text": "Repeating pattern is well represented in predictions", "bbox": {"l": 293.64209, "t": 465.59784, "r": 437.50800000000004, "b": 471.53299, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 13, "text": "Repeating pattern of", "bbox": {"l": 181.89114, "t": 288.35962000000006, "r": 239.23492, "b": 294.2947700000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 14, "text": "horizontally merged cells", "bbox": {"l": 181.89114, "t": 294.89423, "r": 251.52917, "b": 300.82938, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 15, "text": "A", "bbox": {"l": 247.83432, "t": 184.75989000000004, "r": 253.61339, "b": 194.81635000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 16, "text": "Bounding box drifting at the end", "bbox": {"l": 292.18976, "t": 607.80609, "r": 381.54663, "b": 613.7412400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "OTSL", "bbox": {"l": 172.27777, "t": 381.36288, "r": 180.18666, "b": 403.40067, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "HTML", "bbox": {"l": 172.27747, "t": 555.7769499999999, "r": 180.18663, "b": 578.7478, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301633238792419, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007189273834229, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610870242118835, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730682373046875, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610870242118835, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927640914916992, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730682373046875, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617277979850769, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.975471019744873, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9156444668769836, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806178212165833, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873480796813965, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868757724761963, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403367638587952, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698706269264221, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. 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The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. 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Recently, transformer-based models have", "bbox": {"l": 163.1111, "t": 281.3229099999999, "r": 452.24246, "b": 289.39267, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "demonstrated that table-structure can be recognized with impressive ac-", "bbox": {"l": 163.1111, "t": 292.28189, "r": 452.24792, "b": 300.35165000000006, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "bbox": {"l": 163.1111, "t": 303.24088, "r": 452.2407799999999, "b": 311.31064, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "bbox": {"l": 163.1111, "t": 314.19888, "r": 452.24609, "b": 322.26865, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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Since the", "bbox": {"l": 163.1111, "t": 325.15787, "r": 452.24615000000006, "b": 333.22763, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "token representation of the table structure has a significant impact on", "bbox": {"l": 163.1111, "t": 336.11685, "r": 452.24707, "b": 344.18661, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "bbox": {"l": 163.1111, "t": 347.07584, "r": 452.2459999999999, "b": 355.1456, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "tigate in this paper how table-structure representation can be optimised.", "bbox": {"l": 163.1111, "t": 358.03482, "r": 452.2479900000001, "b": 366.10458, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "We propose a new, optimised table-structure language (OTSL) with a", "bbox": {"l": 163.1111, "t": 368.9938, "r": 452.24609, "b": 377.06357, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "bbox": {"l": 163.1111, "t": 379.95279, "r": 452.2417, "b": 388.02255, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens", "bbox": {"l": 163.1111, "t": 390.91177, "r": 452.2443200000001, "b": 398.98154, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "the sequence length to half of HTML on average. Consequently, model", "bbox": {"l": 163.1111, "t": 401.87076, "r": 452.24878000000007, "b": 409.94052, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "accuracy improves significantly, inference time is halved compared to", "bbox": {"l": 163.1111, "t": 412.82974, "r": 452.24063000000007, "b": 420.8995100000001, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "HTML-based models, and the predicted table structures are always syn-", "bbox": {"l": 163.1111, "t": 423.78774999999996, "r": 452.24161, "b": 431.85751000000005, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "tactically correct. This in turn eliminates most post-processing needs.", "bbox": {"l": 163.1111, "t": 434.74673, "r": 452.24429, "b": 442.8165, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Popular table structure data-sets will be published in OTSL format to", "bbox": {"l": 163.1111, "t": 445.70572000000004, "r": 452.24603, "b": 453.77547999999996, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "the community.", "bbox": {"l": 163.1111, "t": 456.6647, "r": 225.56116, "b": 464.73447, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.1111, "t": 478.69394, "r": 452.24158, "b": 497.78549, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"id": 59, "text": "Keywords:", "bbox": {"l": 163.1111, "t": 478.69394, "r": 211.94211, "b": 486.62024, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "bbox": {"l": 216.55208999999996, "t": 478.75671, "r": 452.24158, "b": 486.82648, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "formers \u00b7 Optimization.", "bbox": {"l": 163.11111, "t": 489.71573, "r": 257.64185, "b": 497.78549, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76512, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"id": 62, "text": "1", "bbox": {"l": 134.76512, "t": 522.11969, "r": 141.48872, "b": 532.68808, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Introduction", "bbox": {"l": 154.93832, "t": 522.11969, "r": 228.93384, "b": 532.68808, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5959500000001, "b": 628.81451, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"id": 64, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "bbox": {"l": 134.76512, "t": 548.2865400000001, "r": 480.5939, "b": 557.0835099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "manuals, specification sheets or marketing material. They often encode highly", "bbox": {"l": 134.76512, "t": 560.24254, "r": 480.59180000000003, "b": 569.0395100000001, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "valuable information and therefore need to be extracted with high accuracy.", "bbox": {"l": 134.76512, "t": 572.19754, "r": 480.59283000000005, "b": 580.99451, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "bbox": {"l": 134.76512, "t": 584.15254, "r": 480.5959500000001, "b": 592.9495099999999, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "ture, making it difficult to recover their correct structure with simple analyt-", "bbox": {"l": 134.76512, "t": 596.10754, "r": 480.58688, "b": 604.90451, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "ical methods. Therefore, accurate table extraction is achieved these days with", "bbox": {"l": 134.76512, "t": 608.06255, "r": 480.59088, "b": 616.85951, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "machine-learning based methods.", "bbox": {"l": 134.76512, "t": 620.01755, "r": 279.32745, "b": 628.81451, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76512, "t": 632.14755, "r": 480.59583, "b": 664.85453, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"id": 71, "text": "In modern document understanding systems [1,15], table extraction is typi-", "bbox": {"l": 149.70811, "t": 632.14755, "r": 480.58899, "b": 640.94452, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "bbox": {"l": 134.76512, "t": 644.1025500000001, "r": 480.59583, "b": 652.8995199999999, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "box, and secondly, their logical row and column structure is recognized. As of", "bbox": {"l": 134.76512, "t": 656.05756, "r": 480.59496999999993, "b": 664.85453, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"id": 74, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "bbox": {"l": 18.340218, "t": 209.47997999999995, "r": 36.339787, "b": 555.00003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, "r": 414.10443, "b": 311.89532, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "C", "bbox": {"l": 407.56335, "t": 314.40619, "r": 414.12564, "b": 322.9317, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "C", "bbox": {"l": 418.51108, "t": 292.08502000000004, "r": 425.07336, "b": 300.61053000000004, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "C", "bbox": {"l": 429.59744, "t": 292.09106, "r": 436.1597300000001, "b": 300.61658, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "C", "bbox": {"l": 440.68759000000006, "t": 292.01230000000004, "r": 447.24987999999996, "b": 300.53781000000004, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "C", "bbox": {"l": 418.6232, "t": 303.29483, "r": 425.18549, "b": 311.82034, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "C", "bbox": {"l": 429.7095299999999, "t": 303.30011, "r": 436.27182, "b": 311.82562, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "C", "bbox": {"l": 440.7996800000001, "t": 303.22211, "r": 447.36197, "b": 311.74762, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "C", "bbox": {"l": 418.62546, "t": 314.56903, "r": 425.18774, "b": 323.09454, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "C", "bbox": {"l": 429.71181999999993, "t": 314.57434, "r": 436.27411, "b": 323.09985, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "C", "bbox": {"l": 440.80194, "t": 314.49631, "r": 447.36423, "b": 323.02182, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "C", "bbox": {"l": 407.39746, "t": 325.29031, "r": 413.95975, "b": 333.81583, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "C", "bbox": {"l": 418.45959, "t": 325.45316, "r": 425.02188, "b": 333.97867, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "C", "bbox": {"l": 429.54593, "t": 325.4592, "r": 436.10822, "b": 333.98471, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "C", "bbox": {"l": 440.63608, "t": 325.38043, "r": 447.19836, "b": 333.90594, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "NL", "bbox": {"l": 451.89511000000005, "t": 280.15717, "r": 463.51273000000003, "b": 288.68268, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "NL", "bbox": {"l": 452.1557, "t": 291.59875000000005, "r": 463.77332, "b": 300.12427, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "NL", "bbox": {"l": 452.17688000000004, "t": 302.84265, "r": 463.79449000000005, "b": 311.36816, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "NL", "bbox": {"l": 452.09887999999995, "t": 314.12441999999993, "r": 463.71648999999996, "b": 322.6499299999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "NL", "bbox": {"l": 452.29733, "t": 325.46906, "r": 463.91495, "b": 333.99457, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "U", "bbox": {"l": 396.09677, "t": 314.49478, "r": 402.65906, "b": 323.02029000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "U", "bbox": {"l": 395.99829, "t": 325.38876000000005, "r": 402.56058, "b": 333.91428, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 396.27475, "t": 292.27057, "r": 402.83704, "b": 300.79608, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "L", "bbox": {"l": 408.54724, "t": 280.96912, "r": 413.60074, "b": 289.49463, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 430.58966, "t": 280.49725, "r": 435.6431600000001, "b": 289.02277, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 441.08069, "t": 280.38062, "r": 446.13419, "b": 288.90613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "X", "bbox": {"l": 407.97388, "t": 292.13425, "r": 414.03625, "b": 300.65976, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "NL", "bbox": {"l": 441.25640999999996, "t": 411.1807600000001, "r": 452.87402, "b": 419.7062700000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "vocabulary:", "bbox": {"l": 393.75256, "t": 399.7947700000001, "r": 432.48929, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "5", "bbox": {"l": 434.5896000000001, "t": 399.7947700000001, "r": 438.80083999999994, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "tokens", "bbox": {"l": 440.90573, "t": 399.7947700000001, "r": 463.22235, "b": 406.89935, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "D OTSL", "bbox": {"l": 384.11816, "t": 258.54718, "r": 413.99307, "b": 265.65179, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "sequence length:", "bbox": {"l": 393.75256, "t": 266.67505000000006, "r": 451.45129000000003, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "30", "bbox": {"l": 453.55083999999994, "t": 266.67505000000006, "r": 461.97485, "b": 273.77966000000004, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vocabulary for this table:", "bbox": {"l": 151.79318, "t": 399.76016, "r": 233.89371000000003, "b": 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"<td", "bbox": {"l": 249.37506000000002, "t": 409.77362, "r": 259.03918, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "colspan=\"2\"", "bbox": {"l": 262.82797, "t": 409.77362, "r": 298.93646, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "colspan=\"3\"", "bbox": {"l": 302.72385, "t": 409.77362, "r": 338.83234, "b": 416.16776, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "rowspan=\"2\"", "bbox": {"l": 152.36208, "t": 418.10522, "r": 190.74123, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "rowspan=\"3\"", "bbox": {"l": 194.52863, "t": 418.10522, "r": 232.90777999999997, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": ">", "bbox": {"l": 236.69518999999997, "t": 418.10522, "r": 240.67617999999996, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "</table>", "bbox": {"l": 244.46358, "t": 418.10522, "r": 269.10144, "b": 424.49936, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "C", "bbox": {"l": 154.50595, "t": 258.60095, "r": 159.62473, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "HTML", "bbox": {"l": 164.74348, "t": 258.60095, "r": 185.21857, "b": 265.70556999999997, "coord_origin": "TOPLEFT"}}, {"id": 116, "text": "sequence length:", "bbox": {"l": 164.3548, "t": 266.49707, "r": 222.05352999999997, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "55", "bbox": {"l": 224.15326, "t": 266.49707, "r": 232.57729, "b": 273.60168, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "today,", "bbox": {"l": 134.765, "t": 452.31378, "r": 161.32928, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "table detection", "bbox": {"l": 164.269, "t": 452.31378, "r": 226.28617999999997, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, 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On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 480.59189, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"id": 3, "text": "Fig. 1.", "bbox": {"l": 134.765, "t": 126.33416999999997, "r": 162.64424, "b": 134.26049999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Comparison between HTML and OTSL table structure representation: (A)", "bbox": {"l": 167.062, "t": 126.39697000000001, "r": 480.59106, "b": 134.46667000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "table-example with complex row and column headers, including a 2D empty span,", "bbox": {"l": 134.765, "t": 137.35595999999998, "r": 480.59018, "b": 145.42566, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "(B)", "bbox": {"l": 134.765, "t": 148.31493999999998, "r": 147.95433, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "minimal graphical representation of table structure using rectangular layout, (C)", "bbox": {"l": 152.39224, "t": 148.31493999999998, "r": 480.59096999999997, "b": 156.38464, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "bbox": {"l": 134.765, "t": 159.27392999999995, "r": 480.59189, "b": 167.34362999999996, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "bbox": {"l": 134.765, "t": 170.23290999999995, "r": 480.58914000000004, "b": 178.30260999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "bbox": {"l": 134.765, "t": 181.19188999999994, "r": 480.59020999999996, "b": 189.26160000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "bbox": {"l": 134.765, "t": 192.15088000000003, "r": 460.87109, "b": 200.22058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"id": 12, "text": "C", "bbox": {"l": 396.41107, "t": 280.98352, "r": 402.97336, "b": 289.50903, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "C", "bbox": {"l": 418.58682, "t": 280.89792, "r": 425.14911, "b": 289.42343, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "C", "bbox": {"l": 395.74835, "t": 303.23727, "r": 402.31064, "b": 311.76279, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "C", "bbox": {"l": 407.54214, "t": 303.36981, 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"coord_origin": "TOPLEFT"}}, {"id": 120, "text": "in documents is a well understood problem, and the latest", "bbox": {"l": 229.992, "t": 452.31378, "r": 480.59232000000003, "b": 461.11075, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "state-of-the-art (SOTA) object detection methods provide an accuracy compa-", "bbox": {"l": 134.76501, "t": 464.26877, "r": 480.59180000000003, "b": 473.06573, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "bbox": {"l": 134.76501, "t": 476.22375, "r": 480.58673, "b": 485.02072, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "bbox": {"l": 134.76501, "t": 488.17975, "r": 480.58658, "b": 496.97672, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "area of research, in which many novel machine learning algorithms are being", "bbox": {"l": 134.76501, "t": 500.13474, "r": 480.58978, "b": 508.9317, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "bbox": {"l": 134.76501, "t": 512.0897199999999, "r": 313.24597, "b": 520.88669, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76501, "t": 524.55072, "r": 480.59482, "b": 664.8547, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"id": 126, "text": "Recently emerging SOTA methods for table structure recognition employ", "bbox": {"l": 149.70901, "t": 524.55072, "r": 480.58884000000006, "b": 533.3476900000001, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "transformer-based models, in which an image of the table is provided to the net-", "bbox": {"l": 134.76501, "t": 536.50671, "r": 480.5917400000001, "b": 545.30368, "coord_origin": "TOPLEFT"}}, {"id": 128, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "bbox": {"l": 134.76501, "t": 548.46172, "r": 480.58868, "b": 557.25868, "coord_origin": "TOPLEFT"}}, {"id": 129, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "bbox": {"l": 134.76501, "t": 560.41672, "r": 480.58795, "b": 569.2136800000001, "coord_origin": "TOPLEFT"}}, {"id": 130, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "bbox": {"l": 134.76501, "t": 572.37172, "r": 480.58978, "b": 581.16869, "coord_origin": "TOPLEFT"}}, {"id": 131, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "bbox": {"l": 134.76501, "t": 584.32672, "r": 480.59479, "b": 593.12369, "coord_origin": "TOPLEFT"}}, {"id": 132, "text": "table structure as rows, columns and spanning cells in various configurations.", "bbox": {"l": 134.76501, "t": 596.28271, "r": 480.58678999999995, "b": 605.0796799999999, "coord_origin": "TOPLEFT"}}, {"id": 133, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "bbox": {"l": 134.76501, "t": 608.23772, "r": 480.59476, "b": 617.03468, "coord_origin": "TOPLEFT"}}, {"id": 134, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "bbox": {"l": 134.76501, "t": 620.19272, "r": 480.5938100000001, "b": 628.98969, "coord_origin": "TOPLEFT"}}, {"id": 135, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "bbox": {"l": 134.76501, "t": 632.1477199999999, "r": 480.59482, "b": 640.94469, "coord_origin": "TOPLEFT"}}, {"id": 136, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "bbox": {"l": 134.76501, "t": 644.10272, "r": 480.58771, "b": 652.89969, "coord_origin": "TOPLEFT"}}, {"id": 137, "text": "the use of HTML as ground-truth representation format for TSR.", "bbox": {"l": 134.76501, "t": 656.05772, "r": 421.45377, "b": 664.8547, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"id": 0, "text": "2", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 118.93377999999996, "r": 480.59183, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"id": 2, "text": "While the majority of research in TSR is currently focused on the develop-", "bbox": {"l": 149.709, "t": 118.93377999999996, "r": 480.59183, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "ment and application of novel neural model architectures, the table structure", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58675999999997, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.5917400000001, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "adopted", "bbox": {"l": 134.765, "t": 154.7998, "r": 169.62514, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "as is", "bbox": {"l": 173.86099, "t": 154.7998, "r": 194.55531, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "for the sequence tokenization in Im2Seq models. In this paper,", "bbox": {"l": 199.60999, "t": 154.7998, "r": 480.58618, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "bbox": {"l": 134.76498, "t": 166.75482, "r": 480.59167, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "bbox": {"l": 134.76498, "t": 178.70983999999999, "r": 480.58968999999996, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "bbox": {"l": 134.76498, "t": 190.66485999999998, "r": 480.5917400000001, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "we select this model to perform our experiments.", "bbox": {"l": 134.76498, "t": 202.61987, "r": 348.35519, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 214.83587999999997, "r": 480.59572999999995, "b": 331.22986, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"id": 12, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "bbox": {"l": 149.70898, "t": 214.83587999999997, "r": 480.5939, "b": 223.63287000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "bbox": {"l": 134.76498, "t": 226.79089, "r": 480.5938100000001, "b": 235.58789000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "bbox": {"l": 134.76498, "t": 238.74689, "r": 480.58667, "b": 247.54387999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "bbox": {"l": 134.76498, "t": 250.70190000000002, "r": 480.5867, "b": 259.49890000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.58771, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "bbox": {"l": 134.76498, "t": 274.61194, "r": 480.59572999999995, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "bbox": {"l": 134.76498, "t": 286.56692999999996, "r": 480.59274, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "allows for syntax validation during inference and ensures a syntactically correct", "bbox": {"l": 134.76498, "t": 298.52190999999993, "r": 480.59473, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "bbox": {"l": 134.76498, "t": 310.47791, "r": 480.58667, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "to HTML.", "bbox": {"l": 134.76498, "t": 322.43289, "r": 179.72021, "b": 331.22986, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 334.64789, "r": 480.59567, "b": 439.08676, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"id": 22, "text": "The paper is structured as follows. In section 2, we give an overview of the", "bbox": {"l": 149.70898, "t": 334.64789, "r": 480.5878000000001, "b": 343.44485000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "latest developments in table-structure reconstruction. In section 3 we review", "bbox": {"l": 134.76498, "t": 346.60388000000006, "r": 480.59375, "b": 355.40085, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "bbox": {"l": 134.76498, "t": 358.55887, "r": 480.58673, "b": 367.3558300000001, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "bbox": {"l": 134.76498, "t": 370.51385, "r": 480.59161, "b": 379.31082, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "bbox": {"l": 134.76498, "t": 382.46883999999994, "r": 480.59177000000005, "b": 391.26581, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "bbox": {"l": 134.76498, "t": 394.42383, "r": 480.58774, "b": 403.2207900000001, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "bbox": {"l": 134.76498, "t": 406.37982, "r": 480.59469999999993, "b": 415.17679, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next", "bbox": {"l": 134.76498, "t": 418.33481, "r": 480.59567, "b": 427.13177, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "potential steps.", "bbox": {"l": 134.76498, "t": 430.28979, "r": 201.27232, "b": 439.08676, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"id": 31, "text": "2", "bbox": {"l": 134.76498, "t": 462.08795, "r": 141.48859, "b": 472.65634, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Related Work", "bbox": {"l": 154.93819, "t": 462.08795, "r": 236.76912999999996, "b": 472.65634, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 488.68582, "r": 484.12047999999993, "b": 664.85577, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"id": 33, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "bbox": {"l": 134.76498, "t": 488.68582, "r": 480.59067, "b": 497.48279, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "documents date back more than two decades [16]. In the recent past, a wide", "bbox": {"l": 134.76498, "t": 500.64081, "r": 480.5917400000001, "b": 509.43777, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "variety of computer vision methods have been explored to tackle the prob-", "bbox": {"l": 134.76498, "t": 512.5957900000001, "r": 480.58971999999994, "b": 521.39276, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "lem of table structure recognition, i.e. the correct identification of columns,", "bbox": {"l": 134.76498, "t": 524.55179, "r": 480.58966, "b": 533.34875, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "bbox": {"l": 134.76498, "t": 536.50679, "r": 480.5897499999999, "b": 545.30376, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "bbox": {"l": 134.76498, "t": 548.4617900000001, "r": 480.58862000000005, "b": 557.2587599999999, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence", "bbox": {"l": 134.76498, "t": 560.41679, "r": 480.59072999999995, "b": 569.21376, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "bbox": {"l": 134.76498, "t": 572.3718, "r": 484.12047999999993, "b": 581.16876, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "bbox": {"l": 134.76498, "t": 584.3267999999999, "r": 480.59567, "b": 593.12376, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "bbox": {"l": 134.76498, "t": 596.28279, "r": 480.58871, "b": 605.07976, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "bbox": {"l": 134.76498, "t": 608.23779, "r": 480.59075999999993, "b": 617.03476, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "bbox": {"l": 134.76498, "t": 620.1927900000001, "r": 480.58574999999996, "b": 628.9897599999999, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "bbox": {"l": 134.76498, "t": 632.1478, "r": 480.58875, "b": 640.94476, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "bbox": {"l": 134.76498, "t": 644.1028, "r": 480.58875, "b": 652.89977, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "bbox": {"l": 134.76498, "t": 656.05879, "r": 480.59069999999997, "b": 664.85577, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"id": 1, "text": "3", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5957599999999, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"id": 3, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59375, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.58774, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "bbox": {"l": 134.765, "t": 142.84479, "r": 480.58675999999997, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "structure representation language, which is often implemented with standard", "bbox": {"l": 134.765, "t": 154.7998, "r": 480.5878000000001, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "bbox": {"l": 134.765, "t": 166.75482, "r": 480.59271, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "bbox": {"l": 134.765, "t": 178.70983999999999, "r": 480.5957599999999, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "bbox": {"l": 134.765, "t": 190.66485999999998, "r": 480.59271, "b": 199.46185000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "in order to obtain the table-structure, which is necessary with OD and GNN", "bbox": {"l": 134.765, "t": 202.61987, "r": 480.59378, "b": 211.41687000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "bbox": {"l": 134.765, "t": 214.57587, "r": 480.58783000000005, "b": 223.37285999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "bbox": {"l": 134.765, "t": 226.53088000000002, "r": 480.58978, "b": 235.32788000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "bbox": {"l": 134.765, "t": 238.48590000000002, "r": 480.59572999999995, "b": 247.28290000000004, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "bbox": {"l": 134.765, "t": 250.44092, "r": 480.59473, "b": 259.23792000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.76498, "t": 262.65692, "r": 480.59569999999997, "b": 486.6467, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"id": 15, "text": "Within the Im2Seq method, we find several popular models, namely the", "bbox": {"l": 149.709, "t": 262.65692, "r": 480.59280000000007, "b": 271.45392000000004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "bbox": {"l": 134.765, "t": 274.61194, "r": 480.59167, "b": 283.40891, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "bbox": {"l": 134.765, "t": 286.56692999999996, "r": 480.59271, "b": 295.36389, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "to predict a table in HTML representation. The", "bbox": {"l": 134.765, "t": 298.52190999999993, "r": 342.02097, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "tag decoder", "bbox": {"l": 345.064, "t": 298.52190999999993, "r": 393.04684, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "predicts a sequence", "bbox": {"l": 397.16699, "t": 298.52190999999993, "r": 480.59082, "b": 307.31888, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "of HTML tags. For each decoded table cell (", "bbox": {"l": 134.76498, "t": 310.47791, "r": 333.29871, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "<td>", "bbox": {"l": 333.29898, "t": 310.47791, "r": 356.9711, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "), the attention is passed to", "bbox": {"l": 357.08499, "t": 310.47791, "r": 480.59433000000007, "b": 319.27487, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "the", "bbox": {"l": 134.76498, "t": 322.43289, "r": 148.59805, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "cell decoder", "bbox": {"l": 152.27698, "t": 322.43289, "r": 202.1109, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "to predict the content with an embedded OCR approach. The", "bbox": {"l": 206.86398, "t": 322.43289, "r": 480.58743, "b": 331.22986, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "bbox": {"l": 134.76498, "t": 334.38788, "r": 480.59476, "b": 343.18484, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "bbox": {"l": 134.76498, "t": 346.34286, "r": 480.58675999999997, "b": 355.13983, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "bbox": {"l": 134.76498, "t": 358.29785, "r": 480.58868, "b": 367.09482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "The predicted cell bounding box is then used to extract text tokens from an", "bbox": {"l": 134.76498, "t": 370.25284, "r": 480.58868, "b": 379.0498, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "bbox": {"l": 134.76498, "t": 382.20883, "r": 480.59357000000006, "b": 391.0058, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "[2]", "bbox": {"l": 134.76498, "t": 394.16382, "r": 144.76979, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "proposes a compact double-matrix representation of table rows and columns", "bbox": {"l": 149.50908, "t": 394.16382, "r": 480.58667, "b": 402.96078, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "to do error detection and error correction of HTML structure sequences based", "bbox": {"l": 134.76498, "t": 406.1188, "r": 480.59569999999997, "b": 414.91576999999995, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "on predictions from [19]. This compact double-matrix representation can not be", "bbox": {"l": 134.76498, "t": 418.07379, "r": 480.59180000000003, "b": 426.87076, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "bbox": {"l": 134.76498, "t": 430.02878, "r": 480.5878000000001, "b": 438.82574, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "bbox": {"l": 134.76498, "t": 441.98376, "r": 480.58868, "b": 450.78073, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "bbox": {"l": 134.76498, "t": 453.93976000000004, "r": 384.5752, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "[5]", "bbox": {"l": 391.55899, "t": 453.93976000000004, "r": 401.73236, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "introduces Gated", "bbox": {"l": 406.55154, "t": 453.93976000000004, "r": 480.58777, "b": 462.73672, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "bbox": {"l": 134.76498, "t": 465.89474, "r": 480.59079, "b": 474.69171, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "extract content.", "bbox": {"l": 134.76498, "t": 477.84973, "r": 203.68625, "b": 486.6467, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 490.06573, "r": 480.59378, "b": 582.54866, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"id": 43, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "bbox": {"l": 149.70898, "t": 490.06573, "r": 480.59378, "b": 498.8627, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "full end-to-end network design that can output the final table structure without", "bbox": {"l": 134.76498, "t": 502.02072, "r": 480.58871, "b": 510.81769, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "bbox": {"l": 134.76498, "t": 513.9757099999999, "r": 480.58675999999997, "b": 522.7726700000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors", "bbox": {"l": 134.76498, "t": 525.93069, "r": 480.58978, "b": 534.72766, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "to investigate if the performance (both in accuracy and inference time) can", "bbox": {"l": 134.76498, "t": 537.8857, "r": 480.58765, "b": 546.6826599999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "be further improved by optimising the table structure representation language.", "bbox": {"l": 134.76498, "t": 549.84169, "r": 480.58971999999994, "b": 558.63866, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "We believe this is a necessary step before further improving neural network", "bbox": {"l": 134.76498, "t": 561.79669, "r": 480.58871, "b": 570.59366, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "architectures for this task.", "bbox": {"l": 134.76498, "t": 573.75169, "r": 249.27811, "b": 582.54866, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.76498, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"id": 51, "text": "3", "bbox": {"l": 134.76498, "t": 605.54984, "r": 141.48859, "b": 616.11823, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Problem Statement", "bbox": {"l": 154.93819, "t": 605.54984, "r": 269.62442, "b": 616.11823, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"id": 53, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "bbox": {"l": 134.76498, "t": 632.14769, "r": 480.59064, "b": 640.94466, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.5867, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "bbox": {"l": 134.76498, "t": 656.0586900000001, "r": 480.5936899999999, "b": 664.85566, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"id": 0, "text": "4", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59378, "b": 187.50684, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"id": 2, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58577999999994, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tokens", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 162.48494, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "<table>", "bbox": {"l": 166.368, "t": 130.88878999999997, "r": 201.74918, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": ",", "bbox": {"l": 201.74899, "t": 130.88878999999997, "r": 204.51561, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "</table>", "bbox": {"l": 208.39699, "t": 130.88878999999997, "r": 248.86904999999996, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": ",", "bbox": {"l": 248.86899, "t": 130.88878999999997, "r": 251.6356, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "<tr>", "bbox": {"l": 255.51698, "t": 130.88878999999997, "r": 278.29846, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ",", "bbox": {"l": 278.29797, "t": 130.88878999999997, "r": 281.06458, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "</tr>", "bbox": {"l": 284.94598, "t": 130.88878999999997, "r": 312.81836, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": ",", "bbox": {"l": 312.81799, "t": 130.88878999999997, "r": 315.58459, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "<td>", "bbox": {"l": 319.466, "t": 130.88878999999997, "r": 343.13812, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "and", "bbox": {"l": 347.13202, "t": 130.88878999999997, "r": 363.17877, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "</td>", "bbox": {"l": 367.06003, "t": 130.88878999999997, "r": 395.82306, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": ", one can construct", "bbox": {"l": 395.82303, "t": 130.88878999999997, "r": 480.59177000000005, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "simple table structures without any spanning cells. In reality though, one needs", "bbox": {"l": 134.76501, "t": 142.84479, "r": 480.59365999999994, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "bbox": {"l": 134.76501, "t": 154.7998, "r": 480.58577999999994, "b": 163.59680000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "bbox": {"l": 134.76501, "t": 166.75482, "r": 480.59378, "b": 175.55182000000002, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the HTML token vocabulary.", "bbox": {"l": 134.76501, "t": 178.70983999999999, "r": 261.92566, "b": 187.50684, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"id": 20, "text": "Fig. 2.", "bbox": {"l": 145.60701, "t": 221.07928000000004, "r": 173.48625, "b": 229.00562000000002, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "bbox": {"l": 176.56001, "t": 221.14209000000005, "r": 469.75223000000005, "b": 229.21178999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.20679, "r": 480.59476, "b": 532.42059, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"id": 22, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "bbox": {"l": 149.709, "t": 368.20679, "r": 480.59283000000005, "b": 377.00375, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "bbox": {"l": 134.765, "t": 380.16177, "r": 480.58664, "b": 388.9587399999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "bbox": {"l": 134.765, "t": 392.11676, "r": 480.59180000000003, "b": 400.91373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "bbox": {"l": 134.765, "t": 404.07175, "r": 480.5897499999999, "b": 412.86871, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "bbox": {"l": 134.765, "t": 416.02774, "r": 480.58871, "b": 424.82471, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "bbox": {"l": 134.765, "t": 427.98273, "r": 480.59265, "b": 436.77969, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "two tokens (", "bbox": {"l": 134.765, "t": 439.9377099999999, "r": 187.93439, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "<td>", "bbox": {"l": 187.931, "t": 439.9377099999999, "r": 211.60313, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "and", "bbox": {"l": 214.75400000000002, "t": 439.9377099999999, "r": 230.80075000000002, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "</td>", "bbox": {"l": 233.83898999999997, "t": 439.9377099999999, "r": 262.60202, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "). Furthermore, when tokenizing the HTML struc-", "bbox": {"l": 262.716, "t": 439.9377099999999, "r": 480.59009, "b": 448.73467999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "ture, one needs to explicitly enumerate possible column-spans and row-spans", "bbox": {"l": 134.76501, "t": 451.8927, "r": 480.58777, "b": 460.68967, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "bbox": {"l": 134.76501, "t": 463.84769, "r": 480.58681999999993, "b": 472.64465, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "bbox": {"l": 134.76501, "t": 475.80368, "r": 480.58681999999993, "b": 484.60065, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "bbox": {"l": 134.76501, "t": 487.75867, "r": 480.59067, "b": 496.55563, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "bbox": {"l": 134.76501, "t": 499.71365, "r": 480.59277, "b": 508.51062, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "variable token row-length makes it challenging for models to learn the HTML", "bbox": {"l": 134.76501, "t": 511.66864, "r": 480.59476, "b": 520.46561, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "structure.", "bbox": {"l": 134.76501, "t": 523.62363, "r": 176.92873, "b": 532.42059, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76501, "t": 536.04263, "r": 480.59289999999993, "b": 580.7056, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"id": 40, "text": "Additionally, it would be desirable if the representation would easily allow", "bbox": {"l": 149.70901, "t": 536.04263, "r": 480.59289999999993, "b": 544.8396, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "bbox": {"l": 134.76501, "t": 547.99763, "r": 480.59085, "b": 556.7946000000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "bbox": {"l": 134.76501, "t": 559.95264, "r": 480.58984, "b": 568.7496, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "bbox": {"l": 134.76501, "t": 571.90863, "r": 452.18933, "b": 580.7056, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76501, "t": 584.32663, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"id": 44, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "bbox": {"l": 149.70901, "t": 584.32663, "r": 480.59283000000005, "b": 593.1236, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "cells, serialised in row-major ordering, where each row and each column have", "bbox": {"l": 134.76501, "t": 596.28262, "r": 480.58978, "b": 605.07959, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the same length (while considering row- and column-spans). Furthermore, every", "bbox": {"l": 134.76501, "t": 608.23763, "r": 480.5936899999999, "b": 617.03459, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "bbox": {"l": 134.76501, "t": 620.19263, "r": 480.59091, "b": 628.98959, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "chical manner. Since the number of tokens for each table row and column can", "bbox": {"l": 134.76501, "t": 632.1476299999999, "r": 480.58582, "b": 640.9446, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "vary significantly, especially for large tables with many row- and column-spans,", "bbox": {"l": 134.76501, "t": 644.10263, "r": 480.59180000000003, "b": 652.8996, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "it is complex to verify the consistency of predicted structures during sequence", "bbox": {"l": 134.76501, "t": 656.05763, "r": 480.59473, "b": 664.85461, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"id": 1, "text": "5", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"id": 3, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59479, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "complex syntax rules, simply to deliver valid output.", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 364.62503, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.76498, "t": 143.48279000000002, "r": 480.59569999999997, "b": 295.74191, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"id": 5, "text": "In practice, we observe two major issues with prediction quality when train-", "bbox": {"l": 149.709, "t": 143.48279000000002, "r": 480.58981, "b": 152.27979000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "bbox": {"l": 134.765, "t": 155.43781, "r": 480.59378, "b": 164.23479999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "one hand, we find that on large tables, the visual attention of the model often", "bbox": {"l": 134.765, "t": 167.39282000000003, "r": 480.5867, "b": 176.18982000000005, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "bbox": {"l": 134.765, "t": 179.34784000000002, "r": 480.59476, "b": 188.14484000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "manifests itself in either in an increasing", "bbox": {"l": 134.765, "t": 191.30286, "r": 314.27805, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "location drift", "bbox": {"l": 318.056, "t": 191.30286, "r": 374.08664, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "for proposed table-cells", "bbox": {"l": 378.80899, "t": 191.30286, "r": 480.58594, "b": 200.09984999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "bbox": {"l": 134.76498, "t": 203.25885000000005, "r": 480.58771, "b": 212.05584999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "bbox": {"l": 134.76498, "t": 215.21387000000004, "r": 480.59569999999997, "b": 224.01085999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "bbox": {"l": 134.76498, "t": 227.16887999999994, "r": 480.59454, "b": 235.96587999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "bbox": {"l": 134.76498, "t": 239.12390000000005, "r": 480.58759000000003, "b": 247.92089999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "bbox": {"l": 134.76498, "t": 251.07892000000004, "r": 480.59277, "b": 259.87591999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "impact the TSR model performance, since they reflect not only in the task of", "bbox": {"l": 134.76498, "t": 263.03394000000003, "r": 480.59463999999997, "b": 271.83092999999997, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "pure structure recognition but also in the equally crucial recognition or matching", "bbox": {"l": 134.76498, "t": 274.98992999999996, "r": 480.58978, "b": 283.78693, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "of table cell content.", "bbox": {"l": 134.76498, "t": 286.94495, "r": 223.57262, "b": 295.74191, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.76498, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"id": 20, "text": "4", "bbox": {"l": 134.76498, "t": 320.6311, "r": 141.48859, "b": 331.19949, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Optimised Table Structure Language", "bbox": {"l": 154.93819, "t": 320.6311, "r": 372.50848, "b": 331.19949, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59473, "b": 441.59985, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"id": 22, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "bbox": {"l": 134.76498, "t": 349.11697, "r": 480.59075999999993, "b": 357.91394, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "bbox": {"l": 134.76498, "t": 361.07196000000005, "r": 480.58875, "b": 369.86893, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "designed to express table structure with a minimized vocabulary and a simple", "bbox": {"l": 134.76498, "t": 373.02795, "r": 480.58681999999993, "b": 381.82492, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "set of rules, which are both significantly reduced compared to HTML. At the", "bbox": {"l": 134.76498, "t": 384.98294, "r": 480.58875, "b": 393.77991, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "same time, OTSL enables easy error detection and correction during sequence", "bbox": {"l": 134.76498, "t": 396.93793, "r": 480.58978, "b": 405.73489, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "generation. We further demonstrate how the compact structure representation", "bbox": {"l": 134.76498, "t": 408.89291, "r": 480.59473, "b": 417.68988, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "and minimized sequence length improves prediction accuracy and inference time", "bbox": {"l": 134.76498, "t": 420.8479, "r": 480.58868, "b": 429.64487, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "in the TableFormer architecture.", "bbox": {"l": 134.76498, "t": 432.80289, "r": 276.67325, "b": 441.59985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.76498, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"id": 30, "text": "4.1", "bbox": {"l": 134.76498, "t": 465.87192, "r": 149.40204, "b": 474.67886, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Language Definition", "bbox": {"l": 160.85902, "t": 465.87192, "r": 261.80109, "b": 474.67886, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"id": 32, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "bbox": {"l": 134.76498, "t": 488.99789, "r": 480.58871, "b": 497.79486, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "bbox": {"l": 134.76498, "t": 500.95288, "r": 480.5867, "b": 509.74985, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "grid.", "bbox": {"l": 134.76498, "t": 512.90887, "r": 154.7131, "b": 521.7058400000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"id": 35, "text": "The OTSL vocabulary is comprised of the following tokens:", "bbox": {"l": 149.70898, "t": 525.5018600000001, "r": 409.31137, "b": 534.29883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.99298, "t": 547.96989, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"id": 36, "text": "-", "bbox": {"l": 140.99298, "t": 547.96989, "r": 146.72047, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "\"C\" cell -", "bbox": {"l": 151.70099, "t": 547.97986, "r": 193.20619, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "a new table cell", "bbox": {"l": 196.52199, "t": 547.97986, "r": 263.46564, "b": 556.77682, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "that either has or does not have cell content", "bbox": {"l": 267.815, "t": 547.97986, "r": 460.54443, "b": 556.77682, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.99301, "t": 560.5629, "r": 480.59392999999994, "b": 581.32483, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"id": 40, "text": "-", "bbox": {"l": 140.99301, "t": 560.5629, "r": 146.7205, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "\"L\" cell -", "bbox": {"l": 151.70102, "t": 560.57286, "r": 194.30011, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "left-looking cell", "bbox": {"l": 198.65903, "t": 560.57286, "r": 264.51779, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": ", merging with the left neighbor cell to create a", "bbox": {"l": 264.51804, "t": 560.57286, "r": 480.59392999999994, "b": 569.36983, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "span", "bbox": {"l": 151.70103, "t": 572.52786, "r": 171.67604, "b": 581.32483, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.99304, "t": 585.11189, "r": 480.58856, "b": 605.87383, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"id": 45, "text": "-", "bbox": {"l": 140.99304, "t": 585.11189, "r": 146.72054, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "\"U\" cell -", "bbox": {"l": 151.70105, "t": 585.12186, "r": 194.11086, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "up-looking cell", "bbox": {"l": 197.74805, "t": 585.12186, "r": 259.89474, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": ", merging with the upper neighbor cell to create a", "bbox": {"l": 259.89206, "t": 585.12186, "r": 480.58856, "b": 593.91882, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "span", "bbox": {"l": 151.70105, "t": 597.07686, "r": 171.67606, "b": 605.87383, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.99304, "t": 609.6599, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"id": 50, "text": "-", "bbox": {"l": 140.99304, "t": 609.6599, "r": 146.72054, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "\"X\" cell -", "bbox": {"l": 151.70105, "t": 609.66986, "r": 193.48323, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "cross cell", "bbox": {"l": 196.79904, "t": 609.66986, "r": 236.12042, "b": 618.46683, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": ", to merge with both left and upper neighbor cells", "bbox": {"l": 236.12505, "t": 609.66986, "r": 454.55496, "b": 618.46683, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"id": 54, "text": "-", "bbox": {"l": 140.99304, "t": 622.2538900000001, "r": 146.72054, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "\"NL\" -", "bbox": {"l": 151.70105, "t": 622.26385, "r": 181.99434, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "new-line", "bbox": {"l": 185.31705, "t": 622.26385, "r": 221.46236, "b": 631.06082, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": ", switch to the next row.", "bbox": {"l": 221.46104, "t": 622.26385, "r": 328.61676, "b": 631.06082, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.76505, "t": 644.10286, "r": 480.59280000000007, "b": 664.85484, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"id": 58, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "bbox": {"l": 149.70905, "t": 644.10286, "r": 480.59280000000007, "b": 652.8998300000001, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "conversion to HTML.", "bbox": {"l": 134.76505, "t": 656.05786, "r": 228.22321, "b": 664.85484, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"id": 0, "text": "6", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 33, "text": "L", "bbox": {"l": 411.86395, "t": 168.06195000000002, "r": 417.38943, "b": 177.38373, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "L", "bbox": {"l": 423.33563, "t": 167.93439, "r": 428.86111, "b": 177.25616000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "X", "bbox": {"l": 387.13593, "t": 180.78576999999996, "r": 393.76453, "b": 190.10753999999997, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "C", "bbox": {"l": 282.2594, "t": 244.50878999999998, "r": 289.43457, "b": 253.83056999999997, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "U", "bbox": {"l": 282.11035, "t": 256.85022000000004, "r": 289.28552, "b": 266.172, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "U", "bbox": {"l": 282.40848, "t": 269.13300000000004, "r": 289.58365, "b": 278.45477000000005, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "L", "bbox": {"l": 295.52902, "t": 244.49347, "r": 301.0545, "b": 253.81525, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "L", "bbox": {"l": 307.46613, "t": 244.57372999999995, "r": 312.99161, "b": 253.89550999999994, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "L", "bbox": {"l": 318.76886, "t": 244.44037000000003, "r": 324.29434, "b": 253.76215000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 294.9021, "t": 256.70154, "r": 301.03976, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X X", "bbox": {"l": 307.17743, "t": 256.70154, "r": 325.59039, "b": 266.02332, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "X", "bbox": {"l": 294.78949, "t": 269.25420999999994, "r": 300.92715, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "X X", "bbox": {"l": 307.06482, "t": 269.25420999999994, "r": 325.47778, "b": 278.57599000000005, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "C", "bbox": {"l": 195.93939, "t": 268.74798999999996, "r": 203.11456, "b": 278.06976, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "L", "bbox": {"l": 209.20891, "t": 268.73267, "r": 214.73439, "b": 278.05444, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "L", "bbox": {"l": 221.14551, "t": 268.81293000000005, "r": 226.67099, "b": 278.13469999999995, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "L", "bbox": {"l": 232.44858, "t": 268.67957, "r": 237.97405999999998, "b": 278.00134, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "C", "bbox": {"l": 196.21715, "t": 244.53961000000004, "r": 203.39232, "b": 253.86139000000003, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "C", "bbox": {"l": 250.32143, "t": 244.09813999999994, "r": 257.49661, "b": 253.41992000000005, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "U", "bbox": {"l": 250.17235999999997, "t": 256.43951000000004, "r": 257.34753, "b": 265.76129000000003, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}, {"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": 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"TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"id": 6, "text": "C", "bbox": {"l": 374.49326, "t": 168.59362999999996, "r": 381.66843, "b": 177.91540999999995, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "C", "bbox": {"l": 398.74011, "t": 168.50005999999996, "r": 405.91528, "b": 177.82183999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "C", "bbox": {"l": 373.76862, "t": 192.92553999999996, "r": 380.94379, "b": 202.24730999999997, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 386.66388, "t": 193.07061999999996, "r": 393.83905, "b": 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"coord_origin": "TOPLEFT"}}, {"id": 53, "text": "U", "bbox": {"l": 250.47049000000004, "t": 268.72222999999997, "r": 257.64566, "b": 278.04400999999996, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "1", "bbox": {"l": 334.51135, "t": 242.99463000000003, "r": 337.22485, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "- simple cells: \"C\"", "bbox": {"l": 339.93835, "t": 242.99463000000003, "r": 391.49472, "b": 249.20911, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2", "bbox": {"l": 334.51135, "t": 252.93255999999997, "r": 337.33313, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 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{"id": 70, "label": "text", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 57, "text": "- horizontal merges: \"C\", \"L\"", "bbox": {"l": 340.15491, "t": 252.93255999999997, "r": 421.98624, "b": 259.14703, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 71, "label": "text", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": "3", "bbox": {"l": 334.51135, "t": 262.87048000000004, "r": 337.29868, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 72, "label": "text", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 59, "text": "- vertical merges: \"C\", \"U\"", "bbox": {"l": 340.086, "t": 262.87048000000004, "r": 415.34375, "b": 269.08496, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "list_item", "bbox": {"l": 334.51135, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.5250625610351562, "cells": [{"id": 60, "text": "4", "bbox": {"l": 334.51135, "t": 272.80841, "r": 337.30188, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "- 2d merges: \"C\", \"L\", \"U\", \"X\"", "bbox": {"l": 340.09241, "t": 272.80841, "r": 426.59875, "b": 279.02288999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 73, "label": "text", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 62, "text": "1", "bbox": {"l": 185.67178, "t": 244.04224, "r": 189.35544, "b": 250.25671, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 63, "text": "2", "bbox": {"l": 185.96759, "t": 268.34766, "r": 189.65125, "b": 274.56213, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 75, "label": "text", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 64, "text": "3", "bbox": {"l": 239.34152, "t": 243.62523999999996, "r": 243.02518, "b": 249.83972000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 76, "label": "text", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 65, "text": "4", "bbox": {"l": 271.32852, "t": 243.49390000000005, "r": 275.01218, "b": 249.70836999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 77, "label": "text", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 66, "text": "2", "bbox": {"l": 229.81627, "t": 166.51495, "r": 233.49992000000003, "b": 172.72942999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 78, "label": "text", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 67, "text": "1", "bbox": {"l": 257.24402, "t": 189.961, "r": 260.92767, "b": 196.17548, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 79, "label": "text", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 68, "text": "3", "bbox": {"l": 186.87526, "t": 177.97668, "r": 190.55891, "b": 184.19115999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 80, "label": "text", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 69, "text": "4", "bbox": {"l": 196.48746, "t": 169.01520000000005, "r": 200.17111, "b": 175.22968000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 81, "label": "text", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 70, "text": "A", "bbox": {"l": 169.74728, "t": 167.88225999999997, "r": 175.72659, "b": 175.65039000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 82, "label": "text", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 71, "text": "B", "bbox": {"l": 169.74728, "t": 206.83867999999995, "r": 175.72659, "b": 214.60681, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 83, "label": "text", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 72, "text": "C", "bbox": {"l": 274.29419, "t": 168.27972, "r": 280.2735, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 84, "label": "text", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 73, "text": "D", "bbox": {"l": 359.56152, "t": 168.27972, "r": 365.54083, "b": 176.04785000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 85, "label": "text", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 74, "text": "E", "bbox": {"l": 169.74728, "t": 243.21149000000003, "r": 175.27112, "b": 250.97960999999998, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}, "confidence": 0.9233023524284363, "cells": [{"id": 75, "text": "4.2", "bbox": {"l": 134.765, "t": 305.29581, "r": 149.40205, "b": 314.10275, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "Language Syntax", "bbox": {"l": 160.85904, "t": 305.29581, "r": 246.65197999999998, "b": 314.10275, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"id": 77, "text": "The OTSL representation follows these syntax rules:", "bbox": {"l": 134.765, "t": 325.24777, "r": 363.79617, "b": 334.04474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.97299, "t": 347.17081, "r": 480.58902, "b": 367.93375, "coord_origin": "TOPLEFT"}, "confidence": 0.9588112235069275, "cells": [{"id": 78, "text": "1.", "bbox": {"l": 138.97299, "t": 347.18079, "r": 146.71991, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Left-looking cell rule", "bbox": {"l": 151.70099, "t": 347.17081, "r": 257.37927, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": ": The left neighbour of an \"L\" cell must be either", "bbox": {"l": 257.383, "t": 347.18079, "r": 480.58902, "b": 355.97775, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "another \"L\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 359.13678, "r": 283.59387, "b": 367.93375, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 480.5874, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"id": 2, "text": "Fig. 3.", "bbox": {"l": 134.765, "t": 125.79918999999984, "r": 162.64424, "b": 133.72551999999996, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "bbox": {"l": 166.276, "t": 125.86200000000008, "r": 480.58675999999997, "b": 133.93169999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.5874, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "encoding; E - explanation on cell encoding", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 306.1156, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 3. 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Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.97299, "t": 371.08481, "r": 480.59229000000005, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"id": 82, "text": "2.", "bbox": {"l": 138.97299, "t": 371.09479, "r": 146.71991, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Up-looking cell rule", "bbox": {"l": 151.70099, "t": 371.08481, "r": 252.11203, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": ": The upper neighbour of a \"U\" cell must be either", "bbox": {"l": 252.112, "t": 371.09479, "r": 480.59229000000005, "b": 379.89175, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "another \"U\" cell or a \"C\" cell.", "bbox": {"l": 151.70099, "t": 383.04977, "r": 284.8392, "b": 391.84673999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.97299, "t": 394.99780000000004, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"id": 86, "text": "3.", "bbox": {"l": 138.97299, "t": 395.0077800000001, "r": 146.71991, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Cross cell rule", "bbox": {"l": 151.70099, "t": 394.99780000000004, "r": 223.3042, "b": 403.80475, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": ":", "bbox": {"l": 223.30699, "t": 395.0077800000001, "r": 226.07360999999997, "b": 403.80475, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 439.67371, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"id": 89, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "bbox": {"l": 151.70099, "t": 406.96677, "r": 480.59238, "b": 415.76373, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "bbox": {"l": 151.70099, "t": 418.9217499999999, "r": 480.59219, "b": 427.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "or an \"L\" cell.", "bbox": {"l": 151.70099, "t": 430.87674, "r": 214.39663999999996, "b": 439.67371, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.97299, "t": 442.82574, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"id": 92, "text": "4.", "bbox": {"l": 138.97299, "t": 442.83572, "r": 146.71991, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "First row rule", "bbox": {"l": 151.70099, "t": 442.82574, "r": 221.32263, "b": 451.63269, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.", "bbox": {"l": 221.32700000000003, "t": 442.83572, "r": 474.59018, "b": 451.63269, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.97299, "t": 454.78375, "r": 480.58746, "b": 475.54568, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"id": 95, "text": "5.", "bbox": {"l": 138.97299, "t": 454.7937299999999, "r": 146.71991, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "First column rule", "bbox": {"l": 151.70099, "t": 454.78375, "r": 240.71982, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ": Only \"U\" cells and \"C\" cells are allowed in the first", "bbox": {"l": 240.71599, "t": 454.7937299999999, "r": 480.58746, "b": 463.5907, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "column.", "bbox": {"l": 151.70099, "t": 466.74872, "r": 186.0072, "b": 475.54568, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.97299, "t": 478.69675, "r": 480.59457, "b": 499.45969, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"id": 99, "text": "6.", "bbox": {"l": 138.97299, "t": 478.70673, "r": 146.71991, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Rectangular rule", "bbox": {"l": 151.70099, "t": 478.69675, "r": 235.15768, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": ": The table representation is always rectangular - all rows", "bbox": {"l": 235.15697999999998, "t": 478.70673, "r": 480.59457, "b": 487.50369, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "bbox": {"l": 151.70099, "t": 490.66272, "r": 448.04147, "b": 499.45969, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.76498, "t": 512.59271, "r": 480.59583, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"id": 103, "text": "The application of these rules gives OTSL a set of unique properties. First", "bbox": {"l": 149.70898, "t": 512.59271, "r": 480.59583, "b": 521.38968, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "bbox": {"l": 134.76498, "t": 524.5477000000001, "r": 480.59079, "b": 533.34467, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "bbox": {"l": 134.76498, "t": 536.5027, "r": 480.59482, "b": 545.29967, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "bbox": {"l": 134.76498, "t": 548.4586899999999, "r": 480.58865000000003, "b": 557.25566, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "bbox": {"l": 134.76498, "t": 560.4137000000001, "r": 480.59365999999994, "b": 569.21066, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "bbox": {"l": 134.76498, "t": 572.3687, "r": 480.58673, "b": 581.16566, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "bbox": {"l": 134.76498, "t": 584.3237, "r": 480.59171, "b": 593.12067, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "bbox": {"l": 134.76498, "t": 596.2787, "r": 480.59180000000003, "b": 605.07567, "coord_origin": "TOPLEFT"}}, {"id": 111, "text": "predicted token can be validated straight during sequence generation by looking", "bbox": {"l": 134.76498, "t": 608.2347, "r": 480.5936899999999, "b": 617.03166, "coord_origin": "TOPLEFT"}}, {"id": 112, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "bbox": {"l": 134.76498, "t": 620.1897, "r": 480.59072999999995, "b": 628.98666, "coord_origin": "TOPLEFT"}}, {"id": 113, "text": "predicted sequence is syntactically valid.", "bbox": {"l": 134.76498, "t": 632.1447000000001, "r": 311.19769, "b": 640.9416699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.76498, "t": 644.1026899999999, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"id": 114, "text": "These characteristics can be easily learned by sequence generator networks,", "bbox": {"l": 149.70898, "t": 644.1026899999999, "r": 480.59186, "b": 652.89966, "coord_origin": "TOPLEFT"}}, {"id": 115, "text": "as we demonstrate further below. We find strong indications that this pattern", "bbox": {"l": 134.76498, "t": 656.05769, "r": 480.59265, "b": 664.8546699999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"id": 1, "text": "7", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "500", "bbox": {"l": 158.48466, "t": 544.67065, "r": 162.69737, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "3500", "bbox": {"l": 158.48466, "t": 548.91264, "r": 164.10178, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "150", "bbox": {"l": 158.48466, "t": 553.15465, "r": 162.69737, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "unit", "bbox": {"l": 168.81696, "t": 540.73164, "r": 172.88876, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "unit", "bbox": {"l": 168.81696, "t": 544.67065, "r": 172.88876, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "unit", "bbox": {"l": 168.81696, "t": 548.91264, "r": 172.88876, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "unit", "bbox": {"l": 168.81696, "t": 553.15465, "r": 172.88876, "b": 555.52309, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "2.", "bbox": {"l": 147.30025, "t": 544.67065, "r": 149.70605, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Item", "bbox": {"l": 150.90895, "t": 544.67065, "r": 155.72055, "b": 547.03909, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 147.30025, "t": 548.91264, "r": 149.70605, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Item", "bbox": {"l": 150.90895, "t": 548.91264, "r": 155.72055, "b": 551.28108, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4.", "bbox": {"l": 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the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8301323056221008, "cells": [{"id": 0, "text": "8", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 139.37193, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "page_header", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.81335, "t": 93.77099999999996, "r": 178.07675, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37415, "t": 93.77099999999996, "r": 231.72227, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). 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Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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"TOPLEFT"}}, {"id": 53, "text": "Standardized", "bbox": {"l": 193.53331, "t": 524.51422, "r": 220.31973, "b": 528.7774, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "Images", "bbox": {"l": 199.47311, "t": 529.8167100000001, "r": 214.37889, "b": 534.0799, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "BBox", "bbox": {"l": 273.61066, "t": 509.9053, "r": 284.47275, "b": 514.16849, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Decoder", "bbox": {"l": 270.45187, "t": 513.6928399999999, "r": 287.63242, "b": 517.9560200000001, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "BBoxes", "bbox": {"l": 332.47852, "t": 508.14438, "r": 348.14014, "b": 512.40756, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "BBoxes can be", "bbox": {"l": 376.68622, "t": 521.12024, "r": 407.25497, "b": 525.38342, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "traced back to the", "bbox": {"l": 373.90869, "t": 525.66525, "r": 410.03506, "b": 529.92844, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "original image to", "bbox": {"l": 375.29871, "t": 530.21024, "r": 408.64902, "b": 534.47342, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "extract content", "bbox": {"l": 377.06747, "t": 534.75522, "r": 406.88312, "b": 539.01843, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Structure Tags sequence", "bbox": {"l": 383.56683, "t": 563.24176, "r": 433.76544, "b": 567.50497, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "provide full description of", "bbox": {"l": 383.52768, "t": 567.78676, "r": 433.80764999999997, "b": 572.04997, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "the table structure", "bbox": {"l": 390.47522, "t": 572.33177, "r": 426.85703, "b": 576.59499, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "Structure Tags", "bbox": {"l": 293.94702, "t": 577.89143, "r": 323.1691, "b": 582.15465, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "in OTSL format", "bbox": {"l": 293.94702, "t": 582.43648, "r": 324.59396, "b": 586.69969, "coord_origin": "TOPLEFT"}}, {"id": 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Tree Edit Distance score (TEDs) for", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.58792, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.58871, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.5917400000001, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "cells. The predicted OTSL structures were converted back to HTML format in", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 480.58968999999996, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 139.68579, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"id": 3, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.58884000000006, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "ure 5).", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 163.56389, "b": 139.68579, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"id": 5, "text": "4.3", "bbox": {"l": 134.765, "t": 161.55682000000002, "r": 149.40205, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Error-detection and -mitigation", "bbox": {"l": 160.85904, "t": 161.55682000000002, "r": 319.34708, "b": 170.36377000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.5957599999999, "b": 298.67584, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"id": 7, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "bbox": {"l": 134.765, "t": 182.28179999999998, "r": 480.59572999999995, "b": 191.0788, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "bbox": {"l": 134.765, "t": 194.23779000000002, "r": 480.59473, "b": 203.03479000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "bbox": {"l": 134.765, "t": 206.19281, "r": 480.58678999999995, "b": 214.98981000000003, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "diction correctness. Different heuristics can be used to correct token errors in", "bbox": {"l": 134.765, "t": 218.14783, "r": 480.59177000000005, "b": 226.94482000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "bbox": {"l": 134.765, "t": 230.10284000000001, "r": 480.58768, "b": 238.89984000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "heuristics can be applied either after the prediction of each token, or at the end", "bbox": {"l": 134.765, "t": 242.05786, "r": 480.5867, "b": 250.85486000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "bbox": {"l": 134.765, "t": 254.01288, "r": 480.5938100000001, "b": 262.80988, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "bbox": {"l": 134.765, "t": 265.96887000000004, "r": 480.59069999999997, "b": 274.76586999999995, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "bbox": {"l": 134.765, "t": 277.92389000000003, "r": 480.5957599999999, "b": 286.72086, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "bbox": {"l": 134.765, "t": 289.8788799999999, "r": 469.40369, "b": 298.67584, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"id": 17, "text": "5", "bbox": {"l": 134.765, "t": 321.164, "r": 141.4886, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Experiments", "bbox": {"l": 154.9382, "t": 321.164, "r": 229.03534, "b": 331.73239000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59528, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"id": 19, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "bbox": {"l": 134.765, "t": 347.24985, "r": 480.59375, "b": 356.04681, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "bbox": {"l": 134.765, "t": 359.2048300000001, "r": 480.59476, "b": 368.0018, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "bbox": {"l": 134.765, "t": 371.15982, "r": 480.58786000000003, "b": 379.95679, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "bbox": {"l": 134.765, "t": 383.11481000000003, "r": 480.58777, "b": 391.91177, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the", "bbox": {"l": 134.765, "t": 395.06978999999995, "r": 148.59807, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "canonical", "bbox": {"l": 151.627, "t": 395.06978999999995, "r": 191.84703, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "bbox": {"l": 195.90201, "t": 395.06978999999995, "r": 480.59528, "b": 403.86676, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "found in the first step and evaluate how OTSL impacts the performance of", "bbox": {"l": 134.76501, "t": 407.02478, "r": 480.59283000000005, "b": 415.82175, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TableFormer after training on other publicly available data sets (FinTabNet,", "bbox": {"l": 134.76501, "t": 418.98077, "r": 480.59476, "b": 427.77774, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "bbox": {"l": 134.76501, "t": 430.93576, "r": 480.59171, "b": 439.73273, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "bbox": {"l": 134.76501, "t": 442.8907500000001, "r": 479.30258, "b": 451.6877099999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76501, "t": 484.64813, "r": 480.59082, "b": 503.73965, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"id": 30, "text": "Fig. 4.", "bbox": {"l": 134.76501, "t": 484.64813, "r": 162.64424, "b": 492.57443, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "bbox": {"l": 165.19601, "t": 484.71091, "r": 480.59082, "b": 492.78067, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Im2Seq approach.", "bbox": {"l": 134.76501, "t": 495.66989, "r": 206.70245, "b": 503.73965, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 147.30025, "t": 540.73164, "r": 149.70605, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Item", "bbox": {"l": 150.90895, "t": 540.73164, "r": 155.72055, "b": 543.1000799999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Amount", "bbox": {"l": 162.75987, "t": 535.3938, "r": 172.2963, "b": 537.76224, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "Names", "bbox": {"l": 147.63603, "t": 535.3661500000001, "r": 155.91753, "b": 537.73459, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1000", "bbox": {"l": 158.48466, "t": 540.73164, "r": 164.10178, "b": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 231.43106, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Language", "bbox": {"l": 239.79799999999997, "t": 347.21396, "r": 278.31766, "b": 355.28372, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "TEDs", "bbox": {"l": 324.67001, "t": 341.73495, "r": 348.26419, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "mAP", "bbox": {"l": 396.271, "t": 341.73495, "r": 417.12683, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "(0.75)", "bbox": {"l": 394.927, "t": 352.69394000000005, "r": 418.47278, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Inference", "bbox": {"l": 430.771, "t": 341.73495, "r": 467.1423, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "time (secs)", "bbox": {"l": 427.14801, "t": 352.69394000000005, "r": 470.76056, "b": 360.7637, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "simple", "bbox": {"l": 286.686, "t": 354.68594, "r": 312.33261, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "complex", "bbox": {"l": 320.702, "t": 354.68594, "r": 353.71988, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "all", "bbox": {"l": 369.306, "t": 354.68594, "r": 379.03094, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "6", "bbox": {"l": 161.90601, "t": 373.51596, "r": 166.51294, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "6", "bbox": {"l": 209.509, "t": 373.51596, "r": 214.11594, "b": 381.58572, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 368.03595, "r": 271.40527, "b": 376.10571, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "0.965", "bbox": {"l": 289.017, "t": 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"TOPLEFT"}}, {"id": 46, "text": "0.955", "bbox": {"l": 363.67599, "t": 380.98795, "r": 384.66275, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "0.857", "bbox": {"l": 396.20599, "t": 380.98795, "r": 417.19275, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "5.39", "bbox": {"l": 440.767, "t": 380.98795, "r": 457.14682, "b": 389.05771, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "4", "bbox": {"l": 161.90601, "t": 399.81696, "r": 166.51294, "b": 407.88672, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4", "bbox": {"l": 209.509, "t": 399.81696, "r": 214.11594, "b": 407.88672, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 394.33795, "r": 271.40527, "b": 402.40771, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "0.938", "bbox": {"l": 289.017, "t": 394.33795, "r": 310.00375, "b": 402.40771, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "0.904", "bbox": {"l": 326.71701, "t": 394.33795, "r": 347.70377, 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396.20599, "t": 407.28894, "r": 417.19275, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "3.77", "bbox": {"l": 440.767, "t": 407.28894, "r": 457.14682, "b": 415.3587, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "2", "bbox": {"l": 161.90601, "t": 426.11795, "r": 166.51294, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "4", "bbox": {"l": 209.509, "t": 426.11795, "r": 214.11594, "b": 434.1877099999999, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 420.63895, "r": 271.40527, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "0.923", "bbox": {"l": 289.017, "t": 420.63895, "r": 310.00375, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "0.897", "bbox": {"l": 326.71701, "t": 420.63895, "r": 347.70377, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "0.915", "bbox": {"l": 363.67599, "t": 420.63895, "r": 384.66275, "b": 428.70871, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "0.859", "bbox": {"l": 394.61801, "t": 420.57617, "r": 418.77887, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "1.91", "bbox": {"l": 439.52701, "t": 420.57617, "r": 458.38425, "b": 428.50247, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 433.58994, "r": 272.93954, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "0.945", "bbox": {"l": 289.017, "t": 433.58994, "r": 310.00375, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "0.901", "bbox": {"l": 326.71701, "t": 433.58994, "r": 347.70377, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "0.931", "bbox": {"l": 362.08801, "t": 433.5271599999999, "r": 386.24887, "b": 441.45346, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "0.834", "bbox": {"l": 396.20599, "t": 433.58994, "r": 417.19275, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "3.81", "bbox": {"l": 440.767, "t": 433.58994, "r": 457.14682, "b": 441.6597, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "4", "bbox": {"l": 161.90601, "t": 452.41995, "r": 166.51294, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "2", "bbox": {"l": 209.509, "t": 452.41995, "r": 214.11594, "b": 460.48972, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "OTSL", "bbox": {"l": 246.71000999999998, "t": 446.9399399999999, "r": 271.40527, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "0.952", "bbox": {"l": 289.017, "t": 446.9399399999999, "r": 310.00375, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "0.92", "bbox": {"l": 329.021, "t": 446.9399399999999, "r": 345.40082, "b": 455.0097, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "0.942", "bbox": {"l": 362.08801, "t": 446.87717, "r": 386.24887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "0.857", "bbox": {"l": 394.61801, "t": 446.87717, "r": 418.77887, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "1.22", "bbox": {"l": 439.52701, "t": 446.87717, "r": 458.38425, "b": 454.80347, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "HTML", "bbox": {"l": 245.17598999999998, "t": 459.8919399999999, "r": 272.93954, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "0.944", "bbox": {"l": 289.017, "t": 459.8919399999999, "r": 310.00375, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "0.903", "bbox": {"l": 326.71701, "t": 459.8919399999999, "r": 347.70377, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "0.931", "bbox": {"l": 363.67599, "t": 459.8919399999999, "r": 384.66275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "0.824", "bbox": {"l": 396.20599, "t": 459.8919399999999, "r": 417.19275, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "2", "bbox": {"l": 446.65302, "t": 459.8919399999999, "r": 451.25995, "b": 467.9617, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"id": 1, "text": "9", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": 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Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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467.9617, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"id": 91, "text": "5.2", "bbox": {"l": 134.765, "t": 508.15179, "r": 149.40205, "b": 516.95874, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Quantitative Results", "bbox": {"l": 160.85904, "t": 508.15179, "r": 264.40332, "b": 516.95874, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.5957599999999, "b": 617.03474, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"id": 93, "text": "We picked the model parameter configuration that produced the best prediction", "bbox": {"l": 134.765, "t": 524.55078, "r": 480.59075999999993, "b": 533.34775, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "bbox": {"l": 134.765, "t": 536.50677, "r": 480.58675999999997, "b": 545.3037400000001, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "bbox": {"l": 134.765, "t": 548.4617800000001, "r": 480.59572999999995, "b": 557.25874, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "bbox": {"l": 134.765, "t": 560.41678, "r": 480.59177000000005, "b": 569.21375, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.59579, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"id": 2, "text": "order to compute the TED score. Inference timing results for all experiments", "bbox": {"l": 134.765, "t": 118.93377999999996, "r": 480.5936899999999, "b": 127.73077, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "bbox": {"l": 134.765, "t": 130.88878999999997, "r": 480.59579, "b": 139.68579, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "CPU @2.45 GHz.", "bbox": {"l": 134.765, "t": 142.84479, "r": 210.78462, "b": 151.64178000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"id": 5, "text": "5.1", "bbox": {"l": 134.765, "t": 169.18584999999996, "r": 149.40205, "b": 177.9928, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Hyper Parameter Optimization", "bbox": {"l": 160.85904, "t": 169.18584999999996, "r": 318.44843, "b": 177.9928, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.76498, "t": 185.58582, "r": 480.59277, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"id": 7, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "bbox": {"l": 134.765, "t": 185.58582, "r": 480.59183, "b": 194.38280999999995, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "bbox": {"l": 134.765, "t": 197.54083000000003, "r": 480.59183, "b": 206.33783000000005, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "bbox": {"l": 134.765, "t": 209.49585000000002, "r": 480.59177000000005, "b": 218.29285000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "bbox": {"l": 134.765, "t": 221.45087, "r": 480.59277, "b": 230.24785999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "bbox": {"l": 134.765, "t": 233.40588000000002, "r": 440.94159, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "2x speed", "bbox": {"l": 444.86798, "t": 233.40588000000002, "r": 480.58786000000003, "b": 242.20288000000005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "up", "bbox": {"l": 134.76498, "t": 245.36188000000004, "r": 145.20081, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "in the inference runtime over HTML.", "bbox": {"l": 149.14899, "t": 245.36188000000004, "r": 311.21957, "b": 254.15886999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 480.5954, "b": 327.0408, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"id": 15, "text": "Table", "bbox": {"l": 134.76498, "t": 275.07232999999997, "r": 160.11836, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "1.", "bbox": {"l": 167.34528, "t": 275.07232999999997, "r": 175.59526, "b": 282.9986, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "HPO performed in OTSL and HTML representation on the same", "bbox": {"l": 188.13298, "t": 275.13507000000004, "r": 480.59365999999994, "b": 283.2048300000001, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "bbox": {"l": 134.76498, "t": 286.09409, "r": 480.59444999999994, "b": 294.16385, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "bbox": {"l": 134.76498, "t": 297.05307, "r": 480.5954, "b": 305.12283, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "smaller models trained on OTSL perform better, especially in recognizing complex", "bbox": {"l": 134.76498, "t": 308.01205, "r": 480.59451, "b": 316.08182, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "bbox": {"l": 134.76498, "t": 318.97104, "r": 480.59441999999996, "b": 327.0408, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"id": 22, "text": "#", "bbox": {"l": 160.37, "t": 341.73495, "r": 168.04793, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "enc-layers", "bbox": {"l": 144.592, "t": 354.68594, "r": 183.82806, "b": 362.75570999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "#", "bbox": {"l": 207.974, "t": 341.73495, "r": 215.65193, "b": 349.8047199999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "dec-layers", "bbox": {"l": 192.19499, "t": 354.68594, "r": 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It is clearly evident that the model", "bbox": {"l": 134.765, "t": 572.37178, "r": 480.59069999999997, "b": 581.16875, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "bbox": {"l": 134.765, "t": 584.32678, "r": 480.5957599999999, "b": 593.12375, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "bbox": {"l": 134.765, "t": 596.28278, "r": 480.58774, "b": 605.07974, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "and large tables.", "bbox": {"l": 134.765, "t": 608.2377799999999, "r": 206.78664, "b": 617.03474, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.19278, "r": 480.5957599999999, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"id": 101, "text": "Additionally, the results show that OTSL has an advantage over HTML", "bbox": {"l": 149.709, "t": 620.19278, "r": 480.59271, "b": 628.98975, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "bbox": {"l": 134.765, "t": 632.14778, "r": 480.5957599999999, "b": 640.94475, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "bbox": {"l": 134.765, "t": 644.1027799999999, "r": 480.59283000000005, "b": 652.89975, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "steps which is a result of the reduced sequence representation.", "bbox": {"l": 134.765, "t": 656.0577900000001, "r": 405.79651, "b": 664.8547599999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.98431, "t": 93.77099999999996, "r": 480.59125000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"id": 1, "text": "9", "bbox": 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more accurate bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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bounding boxes with OTSL. In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482728838920593, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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In", "bbox": {"l": 134.765, "t": 321.81577, "r": 480.58889999999997, "b": 330.61273, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "bbox": {"l": 134.765, "t": 333.77075, "r": 480.58681999999993, "b": 342.56772, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "bbox": {"l": 134.765, "t": 345.72574, "r": 480.58981, "b": 354.52271, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "boxes.", "bbox": {"l": 134.765, "t": 357.68073, "r": 161.65704, "b": 366.47769, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes."}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59012, "r": 480.59106, "b": 439.71716, "coord_origin": "TOPLEFT"}, "confidence": 0.9482728838920593, "cells": [{"id": 62, "text": "Fig. 5.", "bbox": {"l": 134.765, "t": 397.59012, "r": 162.64424, "b": 405.51642, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "The OTSL model produces more accurate bounding boxes with less over-", "bbox": {"l": 167.384, "t": 397.65289, "r": 480.59106, "b": 405.72266, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "lap (E) than the HTML model (D), when predicting the structure of a sparse ta-", "bbox": {"l": 134.765, "t": 408.61190999999997, "r": 480.59106, "b": 416.68167000000005, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "ble (A), at twice the inference speed because of shorter sequence length (B),(C).", "bbox": {"l": 134.765, "t": 419.57089, "r": 480.58838000000003, "b": 427.64066, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "\"PMC2807444_006_00.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 430.52987999999993, "r": 304.69171, "b": 438.59964, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "\u03bc", "bbox": {"l": 342.63354, "t": 430.19678, "r": 344.81915, "b": 439.71716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc"}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, "cells": [{"id": 67, "text": "<table>", "bbox": {"l": 180.12473, "t": 516.2332200000001, "r": 190.62042, "b": 518.94992, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "<tr><td></td><td colspan=\"4\"></td><td colspan=\"6\"></td><td colspan=\"3\"></td></tr>", "bbox": {"l": 183.2438, "t": 520.13208, "r": 304.54797, "b": 522.84879, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": 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The OTSL model (B) captured repeating pattern of horizontally", "bbox": {"l": 134.765, "t": 136.82097999999996, "r": 480.58823, "b": 144.89068999999995, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "bbox": {"l": 134.765, "t": 147.77997000000005, "r": 480.5881999999999, "b": 155.84966999999995, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and", "bbox": {"l": 134.765, "t": 158.73895000000005, "r": 480.58838000000003, "b": 166.80864999999994, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "bbox": {"l": 134.765, "t": 169.69794000000002, "r": 415.84454, "b": 177.76764000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39285278320312, "t": 181.9665069580078, "r": 447.35137939453125, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"id": 8, "text": "B", "bbox": {"l": 171.5049, "t": 312.45032, "r": 177.59613, "b": 320.36386, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "C", "bbox": {"l": 171.05823, "t": 492.65274, "r": 177.14946, "b": 500.56628, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Incorrect end of HTML sequence", "bbox": {"l": 283.047, "t": 627.48166, "r": 374.96332, "b": 633.4168099999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Horizontally merged cells are not present", "bbox": {"l": 283.047, "t": 617.35776, "r": 398.05978, "b": 623.29291, "coord_origin": 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"r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9301655888557434, "cells": [{"id": 0, "text": "Optimized Table Tokenization for Table Structure Recognition", "bbox": {"l": 194.478, "t": 93.77099999999996, "r": 447.54291000000006, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9007201790809631, "cells": [{"id": 1, "text": "11", "bbox": {"l": 471.37561, "t": 93.77099999999996, "r": 480.5894799999999, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. 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Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610832691192627, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927664756774902, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617196321487427, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.9754673838615417, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_header", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8610832691192627, "cells": [{"id": 0, "text": "12", "bbox": {"l": 134.765, "t": 93.77099999999996, "r": 143.97887, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "12"}, {"label": "page_header", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8927664756774902, "cells": [{"id": 1, "text": "M.", "bbox": {"l": 167.82053, "t": 93.77099999999996, "r": 178.08249, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Lysak, et al.", "bbox": {"l": 182.37929, "t": 93.77099999999996, "r": 231.72049000000004, "b": 101.84069999999997, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "M. Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.37097, "t": 567.51884, "r": 480.5920100000001, "b": 608.46561, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"id": 42, "text": "2.", "bbox": {"l": 139.37097, "t": 567.51884, "r": 145.94186, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "bbox": {"l": 150.16624, "t": 567.51884, "r": 480.58636, "b": 575.58861, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "in the wild using transformer and identity matrix-based augmentation. In: Porwal,", "bbox": {"l": 151.51797, "t": 578.47784, "r": 480.59012, "b": 586.5476100000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545-", "bbox": {"l": 151.51797, "t": 589.43684, "r": 480.5920100000001, "b": 597.50661, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "561. Springer International Publishing, Cham (2022)", "bbox": {"l": 151.51797, "t": 600.39584, "r": 364.17856, "b": 608.46561, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)"}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 480.58731000000006, "b": 631.18761, "coord_origin": "TOPLEFT"}, "confidence": 0.9617196321487427, "cells": [{"id": 47, "text": "3.", "bbox": {"l": 139.37097, "t": 612.1588399999999, "r": 146.4379, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table", "bbox": {"l": 150.98117, "t": 612.1588399999999, "r": 480.58731000000006, "b": 620.22861, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "bbox": {"l": 151.51797, "t": 623.11784, "r": 400.22525, "b": 631.18761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 139.37097, "t": 634.88084, "r": 480.58826, "b": 664.86761, "coord_origin": "TOPLEFT"}, "confidence": 0.9754673838615417, "cells": [{"id": 50, "text": "4.", "bbox": {"l": 139.37097, "t": 634.88084, "r": 146.52443, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific", "bbox": {"l": 151.12335, "t": 634.88084, "r": 480.58826, "b": 642.95061, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "table recognition. In: 2019 International Conference on Document Analysis and", "bbox": {"l": 151.51797, "t": 645.83984, "r": 480.58752, "b": 653.9096099999999, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "bbox": {"l": 151.51797, "t": 656.79785, "r": 350.11115, "b": 664.86761, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"id": 3, "text": "6", "bbox": {"l": 134.765, "t": 117.54894999999988, "r": 141.4886, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Conclusion", "bbox": {"l": 154.9382, "t": 117.54894999999988, "r": 219.25478999999999, "b": 128.11737000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59569999999997, "b": 203.48181, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"id": 5, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "bbox": {"l": 134.765, "t": 146.86377000000005, "r": 480.59476, "b": 155.66076999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.", "bbox": {"l": 134.765, "t": 158.81879000000004, "r": 480.59476, "b": 167.61577999999997, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "bbox": {"l": 134.765, "t": 170.77380000000005, "r": 480.58978, "b": 179.57079999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "bbox": {"l": 134.765, "t": 182.72979999999995, "r": 480.59569999999997, "b": 191.52679, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "has several key benefits.", "bbox": {"l": 134.765, "t": 194.68480999999997, "r": 239.5387, "b": 203.48181, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44379000000004, "r": 480.59479, "b": 323.83679, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"id": 10, "text": "First and foremost, given the same network configuration, inference time for", "bbox": {"l": 149.709, "t": 207.44379000000004, "r": 480.59283000000005, "b": 216.24077999999997, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "bbox": {"l": 134.765, "t": 219.39880000000005, "r": 480.59365999999994, "b": 228.19579999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "bbox": {"l": 134.765, "t": 231.35382000000004, "r": 480.59079, "b": 240.15081999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "OTSL representation. Additional performance benefits can be obtained with", "bbox": {"l": 134.765, "t": 243.30884000000003, "r": 480.58786000000003, "b": 252.10582999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "bbox": {"l": 134.765, "t": 255.26482999999996, "r": 480.59479, "b": 264.06183, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "bbox": {"l": 134.765, "t": 267.21984999999995, "r": 480.5878000000001, "b": 276.01685, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "bbox": {"l": 134.765, "t": 279.17487000000006, "r": 480.59268, "b": 287.97183, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "bbox": {"l": 134.765, "t": 291.12985, "r": 480.58871, "b": 299.92682, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "inference speed in OTSL with prediction quality comparable to models trained", "bbox": {"l": 134.765, "t": 303.08484, "r": 480.59375, "b": 311.88181, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "on HTML (see Table 1).", "bbox": {"l": 134.765, "t": 315.03983, "r": 240.92351000000002, "b": 323.83679, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.79883, "r": 480.59482, "b": 468.10266, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"id": 20, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "bbox": {"l": 149.709, "t": 327.79883, "r": 480.58984, "b": 336.5957900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "bbox": {"l": 134.765, "t": 339.75482, "r": 480.59473, "b": 348.55179, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "bbox": {"l": 134.765, "t": 351.70981, "r": 480.58664, "b": 360.50677, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "bbox": {"l": 134.765, "t": 363.66479, "r": 480.59479, "b": 372.46176, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "bbox": {"l": 134.765, "t": 375.61978, "r": 480.58971999999994, "b": 384.41675, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "bbox": {"l": 134.765, "t": 387.57477, "r": 480.58673, "b": 396.37173, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "bbox": {"l": 134.765, "t": 399.53076, "r": 480.58574999999996, "b": 408.32773, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "bbox": {"l": 134.765, "t": 411.48575, "r": 480.58675999999997, "b": 420.28271, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "simple and always backwards looking. Each new token can be validated only by", "bbox": {"l": 134.765, "t": 423.44073, "r": 480.59482, "b": 432.23769999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "bbox": {"l": 134.765, "t": 435.39572, "r": 480.58777, "b": 444.19269, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "to detect mistakes. This in return allows to perform structural error detection", "bbox": {"l": 134.765, "t": 447.35071, "r": 480.58968999999996, "b": 456.14767, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "and correction on-the-fly during sequence generation.", "bbox": {"l": 134.765, "t": 459.30569, "r": 366.77698, "b": 468.10266, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"id": 32, "text": "References", "bbox": {"l": 134.765, "t": 493.82083, "r": 197.68642, "b": 504.38922, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 522.87985, "r": 480.5920100000001, "b": 563.87144, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"id": 33, "text": "1.", "bbox": {"l": 139.371, "t": 522.87985, "r": 146.46127, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc-", "bbox": {"l": 151.01955, "t": 522.87985, "r": 480.5920100000001, "b": 530.94962, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "ument conversion as a cloud service with high throughput and responsiveness.", "bbox": {"l": 151.51801, "t": 533.83887, "r": 480.58667, "b": 541.90862, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "CoRR", "bbox": {"l": 151.51801, "t": 544.79785, "r": 176.34149, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "abs/2206.00785", "bbox": {"l": 179.464, "t": 544.73509, "r": 250.67963, "b": 552.66139, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "(2022).", "bbox": {"l": 253.804, "t": 544.79785, "r": 281.9567, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 285.078, "t": 545.44344, "r": 478.03403000000003, "b": 552.91245, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": ",", "bbox": {"l": 478.0319799999999, "t": 544.79785, "r": 480.59099999999995, "b": 552.86761, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "https://doi.org/10.48550/arXiv.2206.00785", "bbox": {"l": 151.51797, "t": 556.4024400000001, "r": 344.474, "b": 563.87144, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. 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diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt b/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt
new file mode 100644
index 00000000..2053f5e4
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt
@@ -0,0 +1,23 @@
+<document>
+<text><location><page_1><loc_12><loc_88><loc_53><loc_94></location>pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.</text>
+<text><location><page_1><loc_12><loc_77><loc_53><loc_86></location>The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.</text>
+<section_header_level_1><location><page_1><loc_12><loc_73><loc_28><loc_75></location>Boots Self-Locking Nut</section_header_level_1>
+<text><location><page_1><loc_12><loc_64><loc_54><loc_73></location>The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.</text>
+<text><location><page_1><loc_12><loc_52><loc_53><loc_62></location>The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.</text>
+<text><location><page_1><loc_12><loc_38><loc_54><loc_50></location>The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.</text>
+<text><location><page_1><loc_12><loc_33><loc_53><loc_36></location>Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is</text>
+<figure>
+<location><page_1><loc_12><loc_10><loc_52><loc_31></location>
+<caption>Figure 7-26. Self-locking nuts.</caption>
+</figure>
+<text><location><page_1><loc_54><loc_85><loc_95><loc_94></location>the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.</text>
+<text><location><page_1><loc_54><loc_83><loc_55><loc_85></location>.</text>
+<section_header_level_1><location><page_1><loc_54><loc_82><loc_76><loc_83></location>Stainless Steel Self-Locking Nut</section_header_level_1>
+<text><location><page_1><loc_54><loc_54><loc_96><loc_81></location>The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.</text>
+<section_header_level_1><location><page_1><loc_54><loc_51><loc_65><loc_52></location>Elastic Stop Nut</section_header_level_1>
+<text><location><page_1><loc_54><loc_47><loc_93><loc_50></location>The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This</text>
+<figure>
+<location><page_1><loc_54><loc_11><loc_94><loc_46></location>
+<caption>Figure 7-27. Stainless steel self-locking nut.</caption>
+</figure>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.json b/tests/data/groundtruth/docling_v2/amt_handbook_sample.json
new file mode 100644
index 00000000..2be08f06
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.json
@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "amt_handbook_sample", "origin": {"mimetype": "application/pdf", "binary_hash": 10189692113572347872, "filename": "amt_handbook_sample.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/pictures/0"}, {"cref": "#/texts/13"}, {"cref": "#/texts/14"}, {"cref": "#/texts/15"}, {"cref": "#/texts/16"}, {"cref": "#/texts/17"}, {"cref": "#/texts/18"}, {"cref": "#/texts/19"}, {"cref": "#/pictures/1"}, {"cref": "#/texts/26"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99212646484375, "t": 730.3163452148438, "r": 314.11212158203125, "b": 681.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 244]}], "orig": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 667.8163452148438, "r": 313.15460205078125, "b": 593.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 376]}], "orig": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.99230194091797, "t": 580.1864013671875, "r": 167.27230834960938, "b": 568.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Boots Self-Locking Nut", "text": "Boots Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 565.8163452148438, "r": 318.49224853515625, "b": 491.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 319]}], "orig": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 478.3163757324219, "r": 316.65728759765625, "b": 404.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 332]}], "orig": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 390.8163757324219, "r": 318.8122863769531, "b": 291.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 477]}], "orig": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.99229431152344, "t": 278.3163757324219, "r": 313.91229248046875, "b": 254.34637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 72.0, "t": 71.80239868164062, "r": 184.14828491210938, "b": 60.99040222167969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Figure 7-26. Self-locking nuts.", "text": "Figure 7-26. Self-locking nuts."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 102.4155, "t": 186.23509, "r": 161.3187, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Boots aircraft nut", "text": "Boots aircraft nut"}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 91.685997, "t": 94.690201, "r": 129.77399, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Flexloc nut", "text": "Flexloc nut"}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 162.48109, "t": 94.690201, "r": 207.85629, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Fiber locknut", "text": "Fiber locknut"}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 237.31379999999996, "t": 94.690201, "r": 289.561, "b": 85.07420300000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic stop nut", "text": "Elastic stop nut"}, {"self_ref": "#/texts/12", "parent": {"cref": "#/pictures/0"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 216.9326, "t": 186.23509, "r": 277.7966, "b": 176.61909000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Elastic anchor nut", "text": "Elastic anchor nut"}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.9923095703125, "t": 730.3163452148438, "r": 561.808349609375, "b": 656.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 368]}], "orig": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "text": "the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 655.3163452148438, "r": 325.99542236328125, "b": 643.8463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ".", "text": "."}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 642.6864013671875, "r": 450.99542236328125, "b": 631.3463745117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Stainless Steel Self-Locking Nut", "text": "Stainless Steel Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 628.3163452148438, "r": 568.00439453125, "b": 416.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1015]}], "orig": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 403.1863708496094, "r": 388.50543212890625, "b": 391.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic Stop Nut", "text": "Elastic Stop Nut", "level": 1}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.99542236328125, "t": 388.8163757324219, "r": 552.351318359375, "b": 364.84637451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 321.0, "t": 73.82240295410156, "r": 481.6493225097656, "b": 63.01040267944336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Figure 7-27. Stainless steel self-locking nut.", "text": "Figure 7-27. Stainless steel self-locking nut."}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.1354999999999, "t": 101.2654, "r": 531.16748, "b": 91.35340099999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Tightened nut", "text": "Tightened nut"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 474.3699, "t": 242.1082, "r": 535.23389, "b": 232.1962000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Untightened nut", "text": "Untightened nut"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 456.7558900000001, "t": 342.00259, "r": 487.08388999999994, "b": 332.3866, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Nut case", "text": "Nut case"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.62299, "t": 196.17650000000003, "r": 497.47183000000007, "b": 186.56050000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Threaded nut core", "text": "Threaded nut core"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 448.55081, "t": 220.6794, "r": 507.686, "b": 211.0634, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Locking shoulder", "text": "Locking shoulder"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/1"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 424.78421, "t": 109.88840000000005, "r": 452.10339000000005, "b": 100.27240000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Keyway", "text": "Keyway"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 537.9854125976562, "t": 46.01969909667969, "r": 560.775390625, "b": 33.70970153808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "7-45", "text": "7-45"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 70.59269714355469, "t": 242.77777099609375, "r": 309.863037109375, "b": 79.6090087890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "captions": [{"cref": "#/texts/7"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/20"}, {"cref": "#/texts/21"}, {"cref": "#/texts/22"}, {"cref": "#/texts/23"}, {"cref": "#/texts/24"}, {"cref": "#/texts/25"}], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 320.4467468261719, "t": 352.359375, "r": 558.8576049804688, "b": 81.689208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "captions": [{"cref": "#/texts/19"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 594.0, "height": 774.0}, "image": null, "page_no": 1}}}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.md b/tests/data/groundtruth/docling_v2/amt_handbook_sample.md
new file mode 100644
index 00000000..68657c53
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.md
@@ -0,0 +1,33 @@
+pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.
+
+The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.
+
+## Boots Self-Locking Nut
+
+The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.
+
+The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.
+
+The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.
+
+Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is
+
+Figure 7-26. Self-locking nuts.
+
+<!-- image -->
+
+the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.
+
+.
+
+## Stainless Steel Self-Locking Nut
+
+The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.
+
+## Elastic Stop Nut
+
+The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This
+
+Figure 7-27. Stainless steel self-locking nut.
+
+<!-- image -->
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json b/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json
new file mode 100644
index 00000000..e47c4d50
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@@ -0,0 +1 @@
+[{"page_no": 0, "size": {"width": 594.0, "height": 774.0}, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": [{"id": 18, "label": "text", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 7, "label": "picture", "bbox": {"l": 70.59269714355469, "t": 531.2222290039062, "r": 309.863037109375, "b": 694.3909912109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9858751893043518, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497622847557068, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 \u2044 4 inch, the Rol-top ranges from 1 \u2044 4 inch to 1 \u2044 6 inch, and the bellows type ranges in size from No. 8 up to 3 \u2044 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 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"text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway 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679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497622847557068, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}, {"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7-45"}], "body": [{"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"id": 0, "text": "pulleys, provided the inner race of the bearing is clamped ", "bbox": {"l": 71.992126, "t": 43.68364999999994, "r": 300.23203, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "to the supporting structure by the nut and bolt. Plates must ", "bbox": {"l": 71.992126, "t": 56.18364999999994, "r": 302.00214, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "be attached to the structure in a positive manner to eliminate ", "bbox": {"l": 71.992126, "t": 68.68364999999994, "r": 309.82214, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "rotation or misalignment when tightening the bolts or screws. ", "bbox": {"l": 71.992126, "t": 81.18364999999994, "r": 314.11212, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"id": 4, "text": "The two general types of self-locking nuts currently in use ", "bbox": {"l": 71.992302, "t": 106.18364999999994, "r": 305.15311, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "are the all-metal type and the fiber lock type. For the sake of ", "bbox": {"l": 71.993103, "t": 118.68364999999994, "r": 309.98309, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "simplicity, only three typical kinds of self-locking nuts are ", "bbox": {"l": 71.993103, "t": 131.18364999999994, "r": 302.44312, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "considered in this handbook: the Boots self-locking and the ", "bbox": {"l": 71.993103, "t": 143.68364999999994, "r": 306.25311, "b": 155.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "stainless steel self-locking nuts, representing the all-metal ", "bbox": {"l": 71.993103, "t": 156.18364999999994, "r": 303.9931, "b": 167.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "types; and the elastic stop nut, representing ", "bbox": {"l": 71.993103, "t": 168.68364999999994, "r": 238.6723, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the fiber insert type. ", "bbox": {"l": 234.26460000000003, "t": 168.68364999999994, "r": 313.1546, "b": 180.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"id": 11, "text": "Boots Self-Locking Nut ", "bbox": {"l": 71.992302, "t": 193.81359999999995, "r": 167.27231, "b": 205.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992294, "t": 208.18364999999994, "r": 318.49225, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"id": 12, "text": "The Boots self-locking nut is of one piece, all-metal ", "bbox": {"l": 71.992302, "t": 208.18364999999994, "r": 302.27719, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "construction designed to hold tight despite severe vibration. ", "bbox": {"l": 71.992294, "t": 220.68364999999994, "r": 313.33026, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Note in ", "bbox": {"l": 71.992294, "t": 233.18364999999994, "r": 104.12231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Figure 7-26", "bbox": {"l": 104.12231, "t": 233.31359999999995, "r": 152.05231, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " that it has two sections and is essentially ", "bbox": {"l": 152.05231, "t": 233.18364999999994, "r": 318.49225, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "bbox": {"l": 71.992294, "t": 245.68364999999994, "r": 309.42929, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "two sections are connected with a spring, which is an integral ", "bbox": {"l": 71.992294, "t": 258.18364999999994, "r": 317.76227, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "part of the nut. ", "bbox": {"l": 71.992294, "t": 270.68361999999996, "r": 133.3723, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.65729, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"id": 20, "text": "The spring keeps the locking and load-carrying sections such ", "bbox": {"l": 71.992294, "t": 295.68361999999996, "r": 316.41028, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "a distance apart that the two sets of threads are out of phase ", "bbox": {"l": 71.992294, "t": 308.18361999999996, "r": 312.20731, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "or spaced so that a bolt, which has been screwed through the ", "bbox": {"l": 71.992294, "t": 320.68361999999996, "r": 316.65729, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "load-carrying section, must push the locking section outward ", "bbox": {"l": 71.992294, "t": 333.18361999999996, "r": 315.91229, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "against the force of the spring to engage the threads of the ", "bbox": {"l": 71.992294, "t": 345.68361999999996, "r": 306.34927, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "locking section properly. ", "bbox": {"l": 71.992294, "t": 358.18361999999996, "r": 174.2023, "b": 369.65362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 318.81229, "b": 482.15363, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"id": 26, "text": "The spring, through the medium of the locking section, exerts ", "bbox": {"l": 71.992294, "t": 383.18361999999996, "r": 317.07227, "b": 394.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "a constant locking force on the bolt in the same direction as a ", "bbox": {"l": 71.992294, "t": 395.68361999999996, "r": 318.81229, "b": 407.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "force that would tighten the nut. In this nut, the load-carrying ", "bbox": {"l": 71.992294, "t": 408.18361999999996, "r": 317.5123, "b": 419.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "section has the thread strength of a standard nut of comparable ", "bbox": {"l": 71.992294, "t": 420.68361999999996, "r": 317.31229, "b": 432.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "size, while the locking section presses against the threads of ", "bbox": {"l": 71.992294, "t": 433.18361999999996, "r": 314.45929, "b": 444.65363, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "bbox": {"l": 71.992294, "t": 445.68362, "r": 311.7023, "b": 457.15363, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "applied to the nut loosens it. The nut can be removed and ", "bbox": {"l": 71.992294, "t": 458.18362, "r": 303.35229, "b": 469.65363, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "reused without impairing its efficiency. ", "bbox": {"l": 71.992294, "t": 470.68362, "r": 231.97228999999996, "b": 482.15363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"id": 34, "text": "Boots self-locking nuts are made with three different spring ", "bbox": {"l": 71.992294, "t": 495.68362, "r": 313.34229, "b": 507.15363, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "styles and in various shapes and sizes. The wing type that is ", "bbox": {"l": 71.992294, "t": 508.18362, "r": 313.91229, "b": 519.65363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"id": 36, "text": "the most common ranges in size for No. 6 up to ", "bbox": {"l": 320.99231, "t": 43.68364999999994, "r": 513.74628, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "1", "bbox": {"l": 514.06342, "t": 44.52013999999997, "r": 516.81342, "b": 50.828610000000026, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "\u2044", "bbox": {"l": 516.81342, "t": 43.68364999999994, "r": 518.4834, "b": 55.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "4", "bbox": {"l": 518.25842, "t": 47.72014999999999, "r": 523.00525, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "inch, the ", "bbox": {"l": 525.37872, "t": 47.72014999999999, "r": 560.44843, "b": 54.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Rol-top ranges from ", "bbox": {"l": 320.99841, "t": 56.18364999999994, "r": 404.58441, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "1", "bbox": {"l": 404.48981, "t": 57.02013999999997, "r": 407.23981, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "\u2044", "bbox": {"l": 407.23981, "t": 56.18364999999994, "r": 408.90982, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "4", "bbox": {"l": 408.68481, "t": 60.22014999999999, "r": 413.38376, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "inch to ", "bbox": {"l": 415.73322, "t": 60.22014999999999, "r": 443.92681999999996, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "1", "bbox": {"l": 443.82598999999993, "t": 57.02013999999997, "r": 446.57598999999993, "b": 63.328610000000026, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "\u2044", "bbox": {"l": 446.57598999999993, "t": 56.18364999999994, "r": 448.24600000000004, "b": 67.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "6", "bbox": {"l": 448.22588999999994, "t": 60.22014999999999, "r": 453.12659, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "inch, and the bellows type ", "bbox": {"l": 455.57697, "t": 60.22014999999999, "r": 560.6579, "b": 66.52863000000002, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ranges in size from No. 8 up to ", "bbox": {"l": 320.99591, "t": 68.68364999999994, "r": 447.36591000000004, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "3", "bbox": {"l": 448.79668999999996, "t": 69.52013999999997, "r": 451.54668999999996, "b": 75.82861000000003, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "\u2044", "bbox": {"l": 451.50549, "t": 68.68364999999994, "r": 453.17551, "b": 80.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "8", "bbox": {"l": 453.17542000000003, "t": 72.72014999999999, "r": 458.07175000000007, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "inch. Wing-type nuts are ", "bbox": {"l": 460.51993, "t": 72.72014999999999, "r": 559.78839, "b": 79.02863000000002, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "bbox": {"l": 320.99542, "t": 81.18364999999994, "r": 559.77844, "b": 92.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "bbox": {"l": 320.99542, "t": 93.68364999999994, "r": 557.87738, "b": 105.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "steel, and the bellows type is made of aluminum alloy only. ", "bbox": {"l": 320.99542, "t": 106.18364999999994, "r": 561.80835, "b": 117.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 \u2044 4 inch, the Rol-top ranges from 1 \u2044 4 inch to 1 \u2044 6 inch, and the bellows type ranges in size from No. 8 up to 3 \u2044 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "text", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 58, "text": ". ", "bbox": {"l": 320.99542, "t": 118.68364999999994, "r": 325.99542, "b": 130.15363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"id": 59, "text": "Stainless Steel Self-Locking Nut ", "bbox": {"l": 320.99542, "t": 131.31359999999995, "r": 450.99542, "b": 142.65363000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 568.00439, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"id": 60, "text": "The stainless steel self-locking nut may be spun on and off ", "bbox": {"l": 320.99542, "t": 145.68364999999994, "r": 558.39838, "b": 157.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "by hand as its locking action takes places only when the ", "bbox": {"l": 320.99542, "t": 158.18364999999994, "r": 547.92542, "b": 169.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "nut is seated against a solid surface and tightened. The nut ", "bbox": {"l": 320.99542, "t": 170.68364999999994, "r": 556.50842, "b": 182.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "consists of two parts: a case with a beveled locking shoulder ", "bbox": {"l": 320.99542, "t": 183.18364999999994, "r": 565.11346, "b": 194.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "and key and a thread insert with a locking shoulder and ", "bbox": {"l": 320.99542, "t": 195.68364999999994, "r": 547.93744, "b": 207.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "bbox": {"l": 320.99542, "t": 208.18364999999994, "r": 549.00647, "b": 219.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "bolt easily, because the threaded insert is the proper size ", "bbox": {"l": 320.99542, "t": 220.68364999999994, "r": 549.0755, "b": 232.15363000000002, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "for the bolt. However, when the nut is seated against a solid ", "bbox": {"l": 320.99542, "t": 233.18364999999994, "r": 562.60242, "b": 244.65363000000002, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "surface and tightened, the locking shoulder of the insert is ", "bbox": {"l": 320.99542, "t": 245.68364999999994, "r": 555.11243, "b": 257.15363, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "pulled downward and wedged against the locking shoulder ", "bbox": {"l": 320.99542, "t": 258.18364999999994, "r": 558.74146, "b": 269.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "of the case. This action compresses the threaded insert and ", "bbox": {"l": 320.99542, "t": 270.68361999999996, "r": 557.88538, "b": 282.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "bbox": {"l": 320.99542, "t": 283.18361999999996, "r": 562.3114, "b": 294.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "in ", "bbox": {"l": 320.99542, "t": 295.68361999999996, "r": 331.27542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "Figure 7-27", "bbox": {"l": 331.81543, "t": 295.81363000000005, "r": 379.86542, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": " shows how the key of the case fits into the ", "bbox": {"l": 379.86542, "t": 295.68361999999996, "r": 554.56543, "b": 307.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": "slotted keyway of the insert so that when the case is turned, ", "bbox": {"l": 320.99542, "t": 308.18361999999996, "r": 561.16339, "b": 319.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "the threaded insert is turned with it. Note that the slot is wider ", "bbox": {"l": 320.99542, "t": 320.68361999999996, "r": 568.00439, "b": 332.15362999999996, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "than the key. This permits the slot to be narrowed and the ", "bbox": {"l": 320.99542, "t": 333.18361999999996, "r": 553.46545, "b": 344.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "insert to be compressed when the nut is tightened. ", "bbox": {"l": 320.99542, "t": 345.68361999999996, "r": 523.19543, "b": 357.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"id": 79, "text": "Elastic Stop Nut ", "bbox": {"l": 320.99542, "t": 370.81363000000005, "r": 388.50543, "b": 382.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"id": 80, "text": "The elastic stop nut is a standard nut with the height ", "bbox": {"l": 320.99542, "t": 385.18361999999996, "r": 548.72437, "b": 396.65362999999996, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "increased to accommodate a fiber locking collar. This ", "bbox": {"l": 320.99542, "t": 397.68361999999996, "r": 552.35132, "b": 409.15362999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": [{"id": 18, "label": "text", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 82, "text": "Tightened nut ", "bbox": {"l": 479.1354999999999, "t": 672.7346, "r": 531.16748, "b": 682.646599, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 83, "text": "Untightened nut ", "bbox": {"l": 474.3699, "t": 531.8918, "r": 535.23389, "b": 541.8037999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 84, "text": "Nut case ", "bbox": {"l": 456.7558900000001, "t": 431.99741, "r": 487.08388999999994, "b": 441.6134, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 85, "text": "Threaded nut core ", "bbox": {"l": 434.62299, "t": 577.8235, "r": 497.47183000000007, "b": 587.4395, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 86, "text": "Locking shoulder ", "bbox": {"l": 448.55081, "t": 553.3206, "r": 507.686, "b": 562.9366, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 87, "text": "Keyway ", "bbox": {"l": 424.78421, "t": 664.1116, "r": 452.10339000000005, "b": 673.7275999999999, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "picture", "bbox": {"l": 70.59269714355469, "t": 531.2222290039062, "r": 309.863037109375, "b": 694.3909912109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9858751893043518, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": [{"id": 24, "label": "text", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 88, "text": "Boots aircraft nut ", "bbox": {"l": 102.4155, "t": 587.76491, "r": 161.3187, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 89, "text": "Flexloc nut ", "bbox": {"l": 91.685997, "t": 679.309799, "r": 129.77399, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 90, "text": "Fiber locknut ", "bbox": {"l": 162.48109, "t": 679.309799, "r": 207.85629, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 91, "text": "Elastic stop nut ", "bbox": {"l": 237.31379999999996, "t": 679.309799, "r": 289.561, "b": 688.925797, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 92, "text": "Elastic anchor nut ", "bbox": {"l": 216.9326, "t": 587.76491, "r": 277.7966, "b": 597.38091, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 72.0, "t": 702.197601, "r": 184.14828, "b": 713.009598, "coord_origin": "TOPLEFT"}, "confidence": 0.9449448585510254, "cells": [{"id": 93, "text": "Figure 7-26. ", "bbox": {"l": 72.0, "t": 702.457596, "r": 119.12800000000001, "b": 713.009598, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "Self-locking nuts. ", "bbox": {"l": 119.4023, "t": 702.197601, "r": 184.14828, "b": 712.403599, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 700.177597, "r": 481.64931999999993, "b": 710.989597, "coord_origin": "TOPLEFT"}, "confidence": 0.9497622847557068, "cells": [{"id": 95, "text": "Figure 7-27. ", "bbox": {"l": 321.0, "t": 700.437599, "r": 368.12799, "b": 710.989597, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "Stainless steel self-locking nut. ", "bbox": {"l": 368.40231, "t": 700.177597, "r": 481.64931999999993, "b": 710.383598, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}], "headers": [{"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"id": 97, "text": "7-45 ", "bbox": {"l": 537.98541, "t": 727.980301, "r": 560.77539, "b": 740.290298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "7-45"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/blocks.md.md b/tests/data/groundtruth/docling_v2/blocks.md.md
new file mode 100644
index 00000000..5269e7d8
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/blocks.md.md
@@ -0,0 +1,33 @@
+Unordered list:
+
+- foo
+
+Empty unordered list:
+
+Ordered list:
+
+- bar
+
+Empty ordered list:
+
+Heading:
+
+# my heading
+
+Empty heading:
+
+Indented code block:
+
+```
+print("Hi!")
+```
+
+Empty indented code block:
+
+Fenced code block:
+
+```
+print("Hello world!")
+```
+
+Empty fenced code block:
diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt b/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt
new file mode 100644
index 00000000..386cf997
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/code_and_formula.doctags.txt
@@ -0,0 +1,14 @@
+<document>
+<section_header_level_1><location><page_1><loc_22><loc_83><loc_45><loc_84></location>Java Code Example</section_header_level_1>
+<text><location><page_1><loc_22><loc_63><loc_78><loc_81></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<paragraph><location><page_1><loc_39><loc_61><loc_61><loc_62></location>Listing 1: Simple Java Program</paragraph>
+<code><location><page_1><loc_22><loc_56><loc_55><loc_60></location>public static void print() { System.out.println( "Java Code" ); }</code>
+<text><location><page_1><loc_22><loc_37><loc_78><loc_55></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<section_header_level_1><location><page_2><loc_22><loc_84><loc_32><loc_85></location>Formula</section_header_level_1>
+<text><location><page_2><loc_22><loc_65><loc_80><loc_82></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<text><location><page_2><loc_22><loc_58><loc_80><loc_65></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.</text>
+<formula><location><page_2><loc_47><loc_56><loc_56><loc_57></location></formula>
+<text><location><page_2><loc_22><loc_38><loc_80><loc_55></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<text><location><page_2><loc_22><loc_29><loc_80><loc_38></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.</text>
+<text><location><page_2><loc_22><loc_21><loc_80><loc_29></location>Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.</text>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.json b/tests/data/groundtruth/docling_v2/code_and_formula.json
new file mode 100644
index 00000000..64e69c56
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/code_and_formula.json
@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "code_and_formula", "origin": {"mimetype": "application/pdf", "binary_hash": 2394749058180317456, "filename": "code_and_formula.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}, {"cref": "#/texts/13"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 667.99462890625, "r": 273.4540100097656, "b": 653.6340942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Java Code Example", "text": "Java Code Example", "level": 1}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 642.8859252929688, "r": 477.48065185546875, "b": 501.4163513183594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 303.13299560546875, "t": 96.83694458007812, "r": 308.1142883300781, "b": 86.87435150146484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 717.8846435546875, "r": 191.51429748535156, "b": 703.5241088867188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Formula", "text": "Formula", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 692.7759399414062, "r": 477.48065185546875, "b": 551.3063354492188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 549.3139038085938, "r": 477.4748229980469, "b": 491.53033447265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 369]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 2, "bbox": {"l": 280.5539855957031, "t": 479.4553527832031, "r": 330.69659423828125, "b": 467.6203308105469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "a 2 + 8 = 12", "text": ""}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 459.64996337890625, "r": 477.47589111328125, "b": 318.1803283691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 316.1879577636719, "r": 477.4748229980469, "b": 246.44935607910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 244.4569549560547, "r": 477.4748229980469, "b": 174.71835327148438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 303.13299560546875, "t": 146.7259521484375, "r": 308.1142883300781, "b": 136.7633514404297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}], "pictures": [], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 595.2760009765625, "height": 841.8900146484375}, "image": null, "page_no": 2}}}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.md b/tests/data/groundtruth/docling_v2/code_and_formula.md
new file mode 100644
index 00000000..d3106f9e
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/code_and_formula.md
@@ -0,0 +1,25 @@
+## Java Code Example
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Listing 1: Simple Java Program
+
+```
+public static void print() { System.out.println( "Java Code" ); }
+```
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+## Formula
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.
+
+<!-- formula-not-decoded -->
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
+
+Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.pages.json b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json
new file mode 100644
index 00000000..066a802d
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json
@@ -0,0 +1 @@
+[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Java Code Example", "bbox": {"l": 133.76801, "t": 124.00536999999997, "r": 273.45401, "b": 138.36590999999999, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801, "t": 149.11406999999997, "r": 477.47687, "b": 159.07665999999995, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801, "t": 161.06908999999996, "r": 477.47781000000003, "b": 171.03168000000005, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801, "t": 173.02405, "r": 477.46985, "b": 182.98663, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801, "t": 184.97906, "r": 477.47186, "b": 194.94164999999998, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801, "t": 196.93408, "r": 477.46993999999995, "b": 206.89666999999997, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801, "t": 208.88909999999998, "r": 477.4806500000001, "b": 218.85168, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801, "t": 220.84509000000003, "r": 355.20087, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.60107, "t": 220.84509000000003, "r": 477.47495000000004, "b": 230.80768, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 232.80005000000006, "r": 477.47083, "b": 242.76262999999994, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 244.75507000000005, "r": 477.47092, "b": 254.71765000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 256.71007999999995, "r": 477.46588, "b": 266.67267000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 268.66510000000005, "r": 477.47586, "b": 278.62769000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 280.62103, "r": 351.47672, "b": 290.58365, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Listing 1: Simple Java Program", "bbox": {"l": 236.17598999999998, "t": 301.54204999999996, "r": 375.06958, "b": 311.50467, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "public static void", "bbox": {"l": 134.575, "t": 317.79947000000004, "r": 235.44695, "b": 326.14719, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "print() {", "bbox": {"l": 241.8759, "t": 317.79947000000004, "r": 292.24908, "b": 326.14719, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "System.out.println(", "bbox": {"l": 157.17101, "t": 328.75745, "r": 264.02368, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "\"Java Code\"", "bbox": {"l": 264.15811, "t": 328.75745, "r": 325.35376, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ");", "bbox": {"l": 326.2952, "t": 328.75745, "r": 337.59286, "b": 337.10516000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "}", "bbox": {"l": 134.239, "t": 339.71646, "r": 138.94637, "b": 348.06418, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201, "t": 359.72406, "r": 477.46985, "b": 369.68667999999997, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801, "t": 371.67905, "r": 477.4758, "b": 381.64166000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801, "t": 383.63403, "r": 477.4758, "b": 393.59665, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801, "t": 395.58905, "r": 477.47284, "b": 405.55167, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801, "t": 407.54404, "r": 477.47589, "b": 417.50665, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801, "t": 419.50003000000004, "r": 477.47177000000005, "b": 429.46265, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et", "bbox": {"l": 133.76801, "t": 256.71007999999995, "r": 477.46588, "b": 266.67267000000004, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 268.66510000000005, "r": 477.47586, "b": 278.62769000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 280.62103, "r": 351.47672, "b": 290.58365, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Stet clita kasd gubergren,", "bbox": {"l": 133.76801, "t": 431.45505, "r": 477.47387999999995, "b": 441.41766000000007, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801, "t": 443.41003, "r": 477.47083, "b": 453.37265, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801, "t": 455.36505, "r": 477.47092, "b": 465.32767, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801, "t": 467.32004, "r": 477.46585, "b": 477.28265, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801, "t": 479.27603, "r": 477.47586, "b": 489.23865, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801, "t": 491.23105, "r": 351.47672, "b": 501.19366, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. 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Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711630702018738, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844802021980286, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.8502804636955261, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}, "confidence": 0.95060133934021, "cells": [{"id": 0, "text": "Formula", "bbox": {"l": 133.76801021944917, "t": 124.00537068468714, "r": 191.51430031418315, "b": 138.36590076397772, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Formula"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.4806507833169, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}, "confidence": 0.9878178238868713, "cells": [{"id": 1, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "bbox": {"l": 133.76801021944917, "t": 149.114070823323, "r": 477.47687078331063, "b": 159.0766608783307, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "bbox": {"l": 133.76801021944917, "t": 161.06909088933185, "r": 477.4778107833122, "b": 171.03167094433945, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd", "bbox": {"l": 133.76801021944917, "t": 173.0241009553406, "r": 477.46985078329914, "b": 182.9866910103483, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "bbox": {"l": 133.76801021944917, "t": 184.97906102134914, "r": 477.4718607833024, "b": 194.94165107635683, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "bbox": {"l": 133.76801021944917, "t": 196.93511108736357, "r": 477.4699407832993, "b": 206.89770114237137, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero", "bbox": {"l": 133.76801021944917, "t": 208.890071153372, "r": 477.4806507833169, "b": 218.8526612083799, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "eos et accusam et justo duo dolores et ea rebum.", "bbox": {"l": 133.76801021944917, "t": 220.84509121938083, "r": 355.20087058271434, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Stet clita kasd gubergren,", "bbox": {"l": 362.6010705948545, "t": 220.84509121938083, "r": 477.4749507833075, "b": 230.80767127438855, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 232.8001112853898, "r": 477.4708307833007, "b": 242.76269134039728, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 244.75506135139813, "r": 477.4709207833008, "b": 254.71765140640582, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 256.71008141740685, "r": 477.4658807832926, "b": 266.6726614724146, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 268.6660714834211, "r": 477.47586078330903, "b": 278.6286615384289, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 280.62109154942993, "r": 351.4767205766048, "b": 290.58367160443765, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.76801021944917, "t": 292.5761116154388, "r": 477.47482078330734, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}, "confidence": 0.9855858683586121, "cells": [{"id": 14, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 292.5761116154388, "r": 477.4710707833011, "b": 302.5386916704465, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 304.5310616814471, "r": 477.47293078330415, "b": 314.4936517364549, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 316.48608174745607, "r": 477.47482078330734, "b": 326.4486618024637, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 328.4411018134648, "r": 477.472810783304, "b": 338.40368186847246, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "sed diam nonummy nibh euismod tincidunt.", "bbox": {"l": 133.76801021944917, "t": 340.3970618794788, "r": 326.20752053515014, "b": 350.3596819344868, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.5539904602546, "t": 362.4346620011579, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}, "confidence": 0.8711630702018738, "cells": [{"id": 19, "text": "a", "bbox": {"l": 280.5539904602546, "t": 364.3070620114962, "r": 285.8142404688841, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "2", "bbox": {"l": 285.8200104688936, "t": 362.4346620011579, "r": 289.78809047540335, "b": 369.40844203966316, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "+ 8 = 12", "bbox": {"l": 292.50400047985886, "t": 364.3070620114962, "r": 330.6965905425146, "b": 374.2696820665041, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "a 2 + 8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.76801021944917, "t": 382.24005211051195, "r": 477.47589078330907, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}, "confidence": 0.9880034923553467, "cells": [{"id": 22, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "bbox": {"l": 148.71201024396512, "t": 382.24005211051195, "r": 477.4698807832991, "b": 392.2026621655199, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam", "bbox": {"l": 133.76801021944917, "t": 394.19506217652076, "r": 477.47580078330884, "b": 404.15768223152867, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita", "bbox": {"l": 133.76801021944917, "t": 406.15005224252945, "r": 477.47580078330884, "b": 416.1126722975373, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "bbox": {"l": 133.76801021944917, "t": 418.10507230853824, "r": 477.47284078330404, "b": 428.06768236354606, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod", "bbox": {"l": 133.76801021944917, "t": 430.0600523745468, "r": 477.47589078330907, "b": 440.0226724295547, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "bbox": {"l": 133.76801021944917, "t": 442.01605244056105, "r": 477.4717707833023, "b": 451.97866249556887, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "bbox": {"l": 133.76801021944917, "t": 453.9710625065698, "r": 477.47388078330573, "b": 463.93368256157777, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor", "bbox": {"l": 133.76801021944917, "t": 465.9260525725785, "r": 477.4708307833007, "b": 475.88867262758635, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "bbox": {"l": 133.76801021944917, "t": 477.8810726385873, "r": 477.4709207833008, "b": 487.84368269359516, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et", "bbox": {"l": 133.76801021944917, "t": 489.8360527045959, "r": 477.4658807832926, "b": 499.7986727596038, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea", "bbox": {"l": 133.76801021944917, "t": 501.7910427706045, "r": 477.47586078330903, "b": 511.7536628256125, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "bbox": {"l": 133.76801021944917, "t": 513.7470728366188, "r": 351.4767205766048, "b": 523.7096828916267, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.76801021944917, "t": 525.7020529026275, "r": 477.47482078330734, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}, "confidence": 0.9844802021980286, "cells": [{"id": 34, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 525.7020529026275, "r": 477.4710707833011, "b": 535.6646729576354, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 537.6570729686364, "r": 477.47293078330415, "b": 547.6196930236442, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 549.612063034645, "r": 477.47482078330734, "b": 559.5746730896528, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 561.5670431006536, "r": 477.472810783304, "b": 571.5296631556615, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 573.5220631666624, "r": 477.47479078330724, "b": 583.4846832216704, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 585.4780532326765, "r": 173.0565202839028, "b": 595.4406532876844, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.76801021944917, "t": 597.4330632986853, "r": 477.47482078330734, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}, "confidence": 0.9872003793716431, "cells": [{"id": 40, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "bbox": {"l": 148.71201024396512, "t": 597.4330632986853, "r": 477.4710707833011, "b": 607.3956633536932, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "bbox": {"l": 133.76801021944917, "t": 609.3880633646941, "r": 477.47293078330415, "b": 619.3506634197018, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "bbox": {"l": 133.76801021944917, "t": 621.3430634307027, "r": 477.47482078330734, "b": 631.3056634857105, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "bbox": {"l": 133.76801021944917, "t": 633.2980634967114, "r": 477.472810783304, "b": 643.2606635517193, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "bbox": {"l": 133.76801021944917, "t": 645.2540535627256, "r": 477.47479078330724, "b": 655.2166536177334, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "volutpat.", "bbox": {"l": 133.76801021944917, "t": 657.2090636287344, "r": 173.0565202839028, "b": 667.1716636837423, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}], "headers": [{"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}, "confidence": 0.8502804636955261, "cells": [{"id": 46, "text": "1", "bbox": {"l": 303.13300049729594, "t": 695.1640638383003, "r": 308.1142905054678, "b": 705.1266638933081, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/duck.md.md b/tests/data/groundtruth/docling_v2/duck.md.md
new file mode 100644
index 00000000..2a8d1efb
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/duck.md.md
@@ -0,0 +1,52 @@
+Summer activities
+
+# Swimming in the lake
+
+Duck
+
+Figure 1: This is a cute duckling
+
+## Let’s swim!
+
+To get started with swimming, first lay down in a water and try not to drown:
+
+- You can relax and look around
+- Paddle about
+- Enjoy summer warmth
+
+Also, don’t forget:
+
+- Wear sunglasses
+- Don’t forget to drink water
+- Use sun cream
+
+Hmm, what else…
+
+## Let’s eat
+
+After we had a good day of swimming in the lake, it’s important to eat something nice
+
+I like to eat leaves
+
+Here are some interesting things a respectful duck could eat:
+
+|         | Food                             |   Calories per portion |
+|---------|----------------------------------|------------------------|
+| Leaves  | Ash, Elm, Maple                  |                     50 |
+| Berries | Blueberry, Strawberry, Cranberry |                    150 |
+| Grain   | Corn, Buckwheat, Barley          |                    200 |
+
+And let’s add another list in the end:
+
+- Leaves
+- Berries
+- Grain
+
+And here my listing in code:
+
+```
+Leaves
+
+Berries
+Grain
+```
diff --git a/tests/data/groundtruth/docling_v2/elife-56337.xml.md b/tests/data/groundtruth/docling_v2/elife-56337.xml.md
index 7ff34bbf..9aeffc69 100644
--- a/tests/data/groundtruth/docling_v2/elife-56337.xml.md
+++ b/tests/data/groundtruth/docling_v2/elife-56337.xml.md
@@ -18,7 +18,7 @@ TEs, especially long terminal repeat (LTR) retrotransposons, also known as endog
 
 We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017).
 
-To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).
+To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value&lt;1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).
 
 We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.
 
@@ -38,7 +38,7 @@ While we generally observed that TE-associated gene reactivation is not caused b
 
 ### ETn retrotransposition in Chr4-cl KO and WT mice
 
-IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability.
+IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value&lt;0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability.
 
 We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4—figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1—figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4\_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5’ and 3’ ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate.
 
@@ -74,7 +74,7 @@ All gRNAs were expressed from the pX330-U6-Chimeric\_BB-CBh-hSpCas9 vector (RRID
 
 For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB\_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB\_307019 or Covance Cat# MMS-101P-200, RRID:AB\_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB\_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB\_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB\_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB\_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB\_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.
 
-ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR\_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR\_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR\_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR\_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).
+ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR\_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR\_013291) under high stringency settings (p&lt;1e-10, peak enrichment &gt;20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p&lt;1e-10, peak enrichment &gt;10) or low (p&lt;1e-5, peak enrichment &gt;10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR\_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR\_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (&lt;10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).
 
 ### Luciferase reporter assays
 
@@ -149,7 +149,7 @@ Key resources table:
 ## Figures
 
 Figure 1.: Genome-wide binding patterns of mouse KRAB-ZFPs.
-(A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
+(A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value&lt;1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p&lt;1e-10, peak enrichment &gt;20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
 Figure 1—source data 1.KRAB-ZFP expression in 40 mouse tissues and cell lines (ENCODE).Mean values of replicates are shown as log2 transcripts per million.
 Figure 1—source data 2.Probability heatmap of KRAB-ZFP binding to TEs.Values corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test).
 
@@ -161,7 +161,7 @@ Figure 1—figure supplement 1.: ES cell-specific expression of KRAB-ZFP gene cl
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 Figure 1—figure supplement 2.: KRAB-ZFP binding motifs and their repression activity.
-(A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR < 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
+(A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR &lt; 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
 
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@@ -171,7 +171,7 @@ Figure 1—figure supplement 3.: KRAB-ZFP binding to ETn retrotransposons.
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 Figure 2.: Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells.
-(A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
+(A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value&lt;0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
 Figure 2—source data 1.Differential H3K9me3 and KAP1 distribution in WT and KRAB-ZFP cluster KO ES cells at TE families and KRAB-ZFP bound TE insertions.Differential read counts and statistical testing were determined by DESeq2.
 
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@@ -182,7 +182,7 @@ Figure 2—figure supplement 1.: Epigenetic changes at TEs and TE-borne enhancer
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 Figure 3.: TE-dependent gene activation in KRAB-ZFP cluster KO ES cells.
-(A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
+(A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value&lt;0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value&lt;0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p&lt;0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
 
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@@ -194,7 +194,7 @@ Figure 4—source data 2.Sequences of capture-seq probes used to enrich genomic
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 Figure 4—figure supplement 1.: Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues.
-(A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
+(A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value&lt;0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
 
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@@ -214,7 +214,7 @@ Figure 4—figure supplement 3.: Confirmation of novel ETn insertions identified
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@@ -263,6 +263,6 @@ Figure 4—figure supplement 3.: Confirmation of novel ETn insertions identified
 - J Wang; G Xie; M Singh; AT Ghanbarian; T Raskó; A Szvetnik; H Cai; D Besser; A Prigione; NV Fuchs; GG Schumann; W Chen; MC Lorincz; Z Ivics; LD Hurst; Z Izsvák. Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells. Nature (2014)
 - D Wolf; K Hug; SP Goff. TRIM28 mediates primer binding site-targeted silencing of Lys1,2 tRNA-utilizing retroviruses in embryonic cells. PNAS (2008)
 - G Wolf; D Greenberg; TS Macfarlan. Spotting the enemy within: targeted silencing of foreign DNA in mammalian genomes by the Krüppel-associated box zinc finger protein family. Mobile DNA (2015a)
-- G Wolf; P Yang; AC Füchtbauer; EM Füchtbauer; AM Silva; C Park; W Wu; AL Nielsen; FS Pedersen; TS Macfarlan. The KRAB zinc finger protein ZFP809 is required to initiate epigenetic silencing of endogenous retroviruses. Genes & Development (2015b)
+- G Wolf; P Yang; AC Füchtbauer; EM Füchtbauer; AM Silva; C Park; W Wu; AL Nielsen; FS Pedersen; TS Macfarlan. The KRAB zinc finger protein ZFP809 is required to initiate epigenetic silencing of endogenous retroviruses. Genes &amp; Development (2015b)
 - M Yamauchi; B Freitag; C Khan; B Berwin; E Barklis. Stem cell factor binding to retrovirus primer binding site silencers. Journal of Virology (1995)
 - Y Zhang; T Liu; CA Meyer; J Eeckhoute; DS Johnson; BE Bernstein; C Nusbaum; RM Myers; M Brown; W Li; XS Liu. Model-based analysis of ChIP-Seq (MACS). Genome Biology (2008)
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_04.html.md b/tests/data/groundtruth/docling_v2/example_04.html.md
index e620a999..f204a12a 100644
--- a/tests/data/groundtruth/docling_v2/example_04.html.md
+++ b/tests/data/groundtruth/docling_v2/example_04.html.md
@@ -1,7 +1,7 @@
 # Data Table with Rowspan and Colspan
 
-| Header 1                   | Header 2 & 3 (colspan)     | Header 2 & 3 (colspan)     |
+| Header 1                   | Header 2 &amp; 3 (colspan)     | Header 2 &amp; 3 (colspan)     |
 |----------------------------|----------------------------|----------------------------|
-| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2               | Row 1, Col 3               |
-| Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) |
+| Row 1 &amp; 2, Col 1 (rowspan) | Row 1, Col 2               | Row 1, Col 3               |
+| Row 1 &amp; 2, Col 1 (rowspan) | Row 2, Col 2 &amp; 3 (colspan) | Row 2, Col 2 &amp; 3 (colspan) |
 | Row 3, Col 1               | Row 3, Col 2               | Row 3, Col 3               |
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_05.html.itxt b/tests/data/groundtruth/docling_v2/example_05.html.itxt
new file mode 100644
index 00000000..3fc541a3
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/example_05.html.itxt
@@ -0,0 +1,3 @@
+item-0 at level 0: unspecified: group _root_
+  item-1 at level 1: title: Omitted html and body tags
+    item-2 at level 2: table with [4x3]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_05.html.json b/tests/data/groundtruth/docling_v2/example_05.html.json
new file mode 100644
index 00000000..ae311397
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/example_05.html.json
@@ -0,0 +1,329 @@
+{
+  "schema_name": "DoclingDocument",
+  "version": "1.0.0",
+  "name": "example_05",
+  "origin": {
+    "mimetype": "text/html",
+    "binary_hash": 1499806583410518209,
+    "filename": "example_05.html"
+  },
+  "furniture": {
+    "self_ref": "#/furniture",
+    "children": [],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "body": {
+    "self_ref": "#/body",
+    "children": [
+      {
+        "$ref": "#/texts/0"
+      }
+    ],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "groups": [],
+  "texts": [
+    {
+      "self_ref": "#/texts/0",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [
+        {
+          "$ref": "#/tables/0"
+        }
+      ],
+      "label": "title",
+      "prov": [],
+      "orig": "Omitted html and body tags",
+      "text": "Omitted html and body tags"
+    }
+  ],
+  "pictures": [],
+  "tables": [
+    {
+      "self_ref": "#/tables/0",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "table",
+      "prov": [],
+      "captions": [],
+      "references": [],
+      "footnotes": [],
+      "data": {
+        "table_cells": [
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 0,
+            "end_row_offset_idx": 1,
+            "start_col_offset_idx": 0,
+            "end_col_offset_idx": 1,
+            "text": "Header 1",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 2,
+            "start_row_offset_idx": 0,
+            "end_row_offset_idx": 1,
+            "start_col_offset_idx": 1,
+            "end_col_offset_idx": 3,
+            "text": "Header 2 & 3 (colspan)",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 2,
+            "col_span": 1,
+            "start_row_offset_idx": 1,
+            "end_row_offset_idx": 3,
+            "start_col_offset_idx": 0,
+            "end_col_offset_idx": 1,
+            "text": "Row 1 & 2, Col 1 (rowspan)",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 1,
+            "end_row_offset_idx": 2,
+            "start_col_offset_idx": 1,
+            "end_col_offset_idx": 2,
+            "text": "Row 1, Col 2",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 1,
+            "end_row_offset_idx": 2,
+            "start_col_offset_idx": 2,
+            "end_col_offset_idx": 3,
+            "text": "Row 1, Col 3",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 2,
+            "start_row_offset_idx": 2,
+            "end_row_offset_idx": 3,
+            "start_col_offset_idx": 1,
+            "end_col_offset_idx": 3,
+            "text": "Row 2, Col 2 & 3 (colspan)",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 3,
+            "end_row_offset_idx": 4,
+            "start_col_offset_idx": 0,
+            "end_col_offset_idx": 1,
+            "text": "Row 3, Col 1",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 3,
+            "end_row_offset_idx": 4,
+            "start_col_offset_idx": 1,
+            "end_col_offset_idx": 2,
+            "text": "Row 3, Col 2",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 3,
+            "end_row_offset_idx": 4,
+            "start_col_offset_idx": 2,
+            "end_col_offset_idx": 3,
+            "text": "Row 3, Col 3",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          }
+        ],
+        "num_rows": 4,
+        "num_cols": 3,
+        "grid": [
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Header 1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 2,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 3,
+              "text": "Header 2 & 3 (colspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 2,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 3,
+              "text": "Header 2 & 3 (colspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Row 1 & 2, Col 1 (rowspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Row 1, Col 2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Row 1, Col 3",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Row 1 & 2, Col 1 (rowspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 2,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 3,
+              "text": "Row 2, Col 2 & 3 (colspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 2,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 3,
+              "text": "Row 2, Col 2 & 3 (colspan)",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Row 3, Col 1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Row 3, Col 2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Row 3, Col 3",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ]
+        ]
+      }
+    }
+  ],
+  "key_value_items": [],
+  "pages": {}
+}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/example_05.html.md b/tests/data/groundtruth/docling_v2/example_05.html.md
new file mode 100644
index 00000000..fbb24b62
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/example_05.html.md
@@ -0,0 +1,7 @@
+# Omitted html and body tags
+
+| Header 1                   | Header 2 &amp; 3 (colspan)     | Header 2 &amp; 3 (colspan)     |
+|----------------------------|----------------------------|----------------------------|
+| Row 1 &amp; 2, Col 1 (rowspan) | Row 1, Col 2               | Row 1, Col 3               |
+| Row 1 &amp; 2, Col 1 (rowspan) | Row 2, Col 2 &amp; 3 (colspan) | Row 2, Col 2 &amp; 3 (colspan) |
+| Row 3, Col 1               | Row 3, Col 2               | Row 3, Col 3               |
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/ipa20180000016.md b/tests/data/groundtruth/docling_v2/ipa20180000016.md
index d02144c6..d1cd8c0b 100644
--- a/tests/data/groundtruth/docling_v2/ipa20180000016.md
+++ b/tests/data/groundtruth/docling_v2/ipa20180000016.md
@@ -112,25 +112,25 @@ Examples of the first fluorescent material 71 specifically include fluorescent m
 
 (i−j)MgO.(j/2)Sc₂O₃.kMgF₂.mCaF₂.(1−n)GeO₂.(n/2)Mt₂O₃:zMn⁴⁺ (I)
 
-wherein Mt is at least one selected from the group consisting of Al, Ga, and In, and j, k, m, n, and z are numbers satisfying 2≦i≦4, 0≦j<0.5, 0<k<1.5, 0≦m<1.5, 0<n<0.5, and 0<z<0.05, respectively.
+wherein Mt is at least one selected from the group consisting of Al, Ga, and In, and j, k, m, n, and z are numbers satisfying 2≦i≦4, 0≦j&lt;0.5, 0&lt;k&lt;1.5, 0≦m&lt;1.5, 0&lt;n&lt;0.5, and 0&lt;z&lt;0.05, respectively.
 
 (Ca₁₋p₋qSrpEuq)AlSiN₃ (II)
 
-wherein p and q are numbers satisfying 0≦p≦1.0, 0<q<1.0, and p+q<1.0.
+wherein p and q are numbers satisfying 0≦p≦1.0, 0&lt;q&lt;1.0, and p+q&lt;1.0.
 
 MªvMbwMcfAl₃₋gSigNh (III)
 
-wherein Mª is at least one element selected from the group consisting of Ca, Sr, Ba, and Mg, Mb is at least one element selected from the group consisting of Li, Na, and K, Mc is at least one element selected from the group consisting of Eu, Ce, Tb, and Mn, v, w, f, g, and h are numbers satisfying 0.80≦v≦1.05, 0.80≦w≦1.05, 0.001<f≦0.1, 0≦g≦0.5, and 3.0≦h≦5.0, respectively.
+wherein Mª is at least one element selected from the group consisting of Ca, Sr, Ba, and Mg, Mb is at least one element selected from the group consisting of Li, Na, and K, Mc is at least one element selected from the group consisting of Eu, Ce, Tb, and Mn, v, w, f, g, and h are numbers satisfying 0.80≦v≦1.05, 0.80≦w≦1.05, 0.001&lt;f≦0.1, 0≦g≦0.5, and 3.0≦h≦5.0, respectively.
 
 (Ca₁₋r₋s₋tSrrBasEut)₂Si₅N₈ (IV)
 
-wherein r, s, and t are numbers satisfying 0≦r≦1.0, 0≦s≦1.0, 0<t<1.0, and r+s+t≦1.0.
+wherein r, s, and t are numbers satisfying 0≦r≦1.0, 0≦s≦1.0, 0&lt;t&lt;1.0, and r+s+t≦1.0.
 
 (Ca,Sr)S:Eu (V)
 
 A₂[M¹₁₋uMn⁴⁺uF₆] (VI)
 
-wherein A is at least one selected from the group consisting of K, Li, Na, Rb, Cs, and NH₄⁺, M¹ is at least one element selected from the group consisting of Group 4 elements and Group 14 elements, and u is the number satisfying 0<u<0.2.
+wherein A is at least one selected from the group consisting of K, Li, Na, Rb, Cs, and NH₄⁺, M¹ is at least one element selected from the group consisting of Group 4 elements and Group 14 elements, and u is the number satisfying 0&lt;u&lt;0.2.
 
 The content of the first fluorescent material 71 in the fluorescent member 50 is not particularly limited as long as the R/B ratio is within a range of 2.0 or more and 4.0 or less. The content of the first fluorescent material 71 in the fluorescent member 50 is, for example, 1 part by mass or more, preferably 5 parts by mass or more, and more preferably 8 parts by mass or more, per 100 parts by mass of the sealing material, and is preferably 200 parts by mass or less, more preferably 150 parts by mass or less, and still more preferably 100 parts by mass or less, per 100 parts by mass of the sealing material. When the content of the first fluorescent material 71 in the fluorescent member 50 is within the aforementioned range, the light emitted from the light emitting element 10 can be efficiently subjected to wavelength conversion, and light capable of promoting growth of plant can be emitted from the light emitting device 100.
 
@@ -148,7 +148,7 @@ The second fluorescent material 72 is preferably a fluorescent material having t
 
 (Ln₁₋ₓ₋yCeₓCry)₃M₅O₁₂ (1)
 
-wherein Ln is at least one rare earth element selected from the group consisting of rare earth elements excluding Ce, M is at least one element selected from the group consisting of Al, Ga, and In, and x and y are numbers satisfying 0.0002<x<0.50 and 0.0001<y<0.05, respectively.
+wherein Ln is at least one rare earth element selected from the group consisting of rare earth elements excluding Ce, M is at least one element selected from the group consisting of Al, Ga, and In, and x and y are numbers satisfying 0.0002&lt;x&lt;0.50 and 0.0001&lt;y&lt;0.05, respectively.
 
 In this case, the second fluorescent material 72 has a composition constituting a garnet structure, and therefore is tough against heat, light, and water, has an absorption peak wavelength of excited absorption spectrum in the vicinity of 420 nm or more and 470 nm or less, and sufficiently absorbs the light from the light emitting element 10, thereby enhancing light emitting intensity of the second fluorescent material 72, which is preferred. Furthermore, the second fluorescent material 72 is excited by light having light emission peak wavelength in a range of 380 nm or more and 490 nm or less and emits light having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less.
 
@@ -156,9 +156,9 @@ In the second fluorescent material 72, from the standpoint of stability of a cry
 
 In the second fluorescent material 72, the value of the parameter x is more preferably in a range of 0.0005 or more and 0.400 or less (0.0005≦x≦0.400), and still more preferably in a range of 0.001 or more and 0.350 or less (0.001≦x≦0.350).
 
-In the second fluorescent material 72, the value of the parameter y is preferably in a range of exceeding 0.0005 and less than 0.040 (0.0005<y<0.040), and more preferably in a range of 0.001 or more and 0.026 or less (0.001≦y≦0.026).
+In the second fluorescent material 72, the value of the parameter y is preferably in a range of exceeding 0.0005 and less than 0.040 (0.0005&lt;y&lt;0.040), and more preferably in a range of 0.001 or more and 0.026 or less (0.001≦y≦0.026).
 
-The parameter x is an activation amount of Ce and the value of the parameter x is in a range of exceeding 0.0002 and less than 0.50 (0.0002<x<0.50), and the parameter y is an activation amount of Cr. When the value of the parameter y is in a range of exceeding 0.0001 and less than 0.05 (0.0001<y<0.05), the activation amount of Ce and the activation amount of Cr that are light emission centers contained in the crystal structure of the fluorescent material are within optimum ranges, the decrease of light emission intensity due to the decrease of light emission center can be suppressed, the decrease of light emission intensity due to concentration quenching caused by the increase of the activation amount can be suppressed, and light emission intensity can be enhanced.
+The parameter x is an activation amount of Ce and the value of the parameter x is in a range of exceeding 0.0002 and less than 0.50 (0.0002&lt;x&lt;0.50), and the parameter y is an activation amount of Cr. When the value of the parameter y is in a range of exceeding 0.0001 and less than 0.05 (0.0001&lt;y&lt;0.05), the activation amount of Ce and the activation amount of Cr that are light emission centers contained in the crystal structure of the fluorescent material are within optimum ranges, the decrease of light emission intensity due to the decrease of light emission center can be suppressed, the decrease of light emission intensity due to concentration quenching caused by the increase of the activation amount can be suppressed, and light emission intensity can be enhanced.
 
 ### Production Method of Second Fluorescent Material
 
@@ -218,7 +218,7 @@ wherein M¹¹ is at least one selected from the group consisting of Ca, Sr, Ba,
 
 Si₆₋bAlbObN₈₋b:Eu (ii)
 
-wherein b satisfies 0<b<4.2.
+wherein b satisfies 0&lt;b&lt;4.2.
 
 M¹³Ga₂S₄:Eu (iii)
 
@@ -365,7 +365,7 @@ The above disclosed subject matter shall be considered illustrative, and not res
 
 4. The light emitting device according to claim 2, wherein the another fluorescent material contains a first element Ln containing at least one element selected from the group consisting of rare earth elements excluding Ce, a second element M containing at least one element selected from the group consisting of Al, Ga and In, Ce, and Cr, and has a composition of an aluminate fluorescent material, and when a molar ratio of the second element M is taken as 5, a molar ratio of Ce is a product of a value of a parameter x and 3, and a molar ratio of Cr is a product of a value of a parameter y and 3, the value of the parameter x being in a range of exceeding 0.0002 and less than 0.50, and the value of the parameter y being in a range of exceeding 0.0001 and less than 0.05.
 
-5. The light emitting device according to claim 2, wherein the another fluorescent material has the composition represented by the following formula (I): (Ln₁₋ₓ₋yCeₓCry)₃M₅O₁₂ (I) wherein Ln is at least one rare earth element selected from the group consisting of rare earth elements excluding Ce, M is at least one element selected from the group consisting of Al, Ga, and In, and x and y are numbers satisfying 0.0002<x<0.50 and 0.0001<y<0.05.
+5. The light emitting device according to claim 2, wherein the another fluorescent material has the composition represented by the following formula (I): (Ln₁₋ₓ₋yCeₓCry)₃M₅O₁₂ (I) wherein Ln is at least one rare earth element selected from the group consisting of rare earth elements excluding Ce, M is at least one element selected from the group consisting of Al, Ga, and In, and x and y are numbers satisfying 0.0002&lt;x&lt;0.50 and 0.0001&lt;y&lt;0.05.
 
 6. The light emitting device according to claim 2, the light emitting device being used in plant cultivation.
 
diff --git a/tests/data/groundtruth/docling_v2/mixed.md.md b/tests/data/groundtruth/docling_v2/mixed.md.md
new file mode 100644
index 00000000..6cd5d52b
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/mixed.md.md
@@ -0,0 +1,25 @@
+# Title
+
+Some text
+
+## Famous ducks
+
+Here is a table:
+
+| Character      | Name in German   | Name in French   | Name in Italian   |
+|----------------|------------------|------------------|-------------------|
+| Scrooge McDuck | Dagobert Duck    | Balthazar Picsou | Paperone          |
+| Huey           | Tick             | Riri             | Qui               |
+| Dewey          | Trick            | Fifi             | Quo               |
+| Louie          | Track            | Loulou           | Qua               |
+
+And here is more HTML:
+
+Some paragraph.
+
+Now a div — almost there...
+
+- foo
+- bar
+
+The end!
diff --git a/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt b/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt
new file mode 100644
index 00000000..a86cbe7b
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt
@@ -0,0 +1,15 @@
+<document>
+<section_header_level_1><location><page_1><loc_22><loc_83><loc_41><loc_84></location>Figures Example</section_header_level_1>
+<text><location><page_1><loc_22><loc_63><loc_78><loc_81></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<figure>
+<location><page_1><loc_22><loc_36><loc_78><loc_62></location>
+<caption>Figure 1: This is an example image.</caption>
+</figure>
+<text><location><page_1><loc_22><loc_15><loc_78><loc_30></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.</text>
+<text><location><page_2><loc_22><loc_66><loc_78><loc_84></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.</text>
+<figure>
+<location><page_2><loc_36><loc_36><loc_64><loc_65></location>
+<caption>Figure 2: This is an example image.</caption>
+</figure>
+<text><location><page_2><loc_22><loc_15><loc_78><loc_31></location>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.</text>
+</document>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/picture_classification.json b/tests/data/groundtruth/docling_v2/picture_classification.json
new file mode 100644
index 00000000..424bdca1
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/picture_classification.json
@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.0.0", "name": "picture_classification", "origin": {"mimetype": "application/pdf", "binary_hash": 6445357065749877499, "filename": "picture_classification.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/pictures/0"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/pictures/1"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 667.1912231445312, "r": 252.35513305664062, "b": 654.4518432617188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Figures Example", "text": "Figures Example", "level": 1}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 642.3280639648438, "r": 477.4827575683594, "b": 501.97412109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 226.89100646972656, "t": 262.86505126953125, "r": 384.35479736328125, "b": 254.0182647705078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Figure 1: This is an example image.", "text": "Figure 1: This is an example image."}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 133.76800537109375, "t": 238.95504760742188, "r": 477.4817199707031, "b": 122.51225280761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 747]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua."}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 303.13299560546875, "t": 96.27903747558594, "r": 308.1142883300781, "b": 87.43224334716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 664.1490478515625, "r": 477.4817199707031, "b": 523.7951049804688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 226.89100646972656, "t": 268.7890319824219, "r": 384.35479736328125, "b": 259.9422607421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Figure 2: This is an example image.", "text": "Figure 2: This is an example image."}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.76800537109375, "t": 245.71804809570312, "r": 477.4817199707031, "b": 117.32023620605469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 804]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum."}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 303.13299560546875, "t": 96.27903747558594, "r": 308.1142883300781, "b": 87.43224334716797, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 134.9200439453125, "t": 487.109375, "r": 475.6635437011719, "b": 281.78173828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "captions": [{"cref": "#/texts/2"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 2, "bbox": {"l": 218.8155517578125, "t": 513.984619140625, "r": 391.96246337890625, "b": 283.10589599609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "captions": [{"cref": "#/texts/6"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 2}}}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/picture_classification.md b/tests/data/groundtruth/docling_v2/picture_classification.md
new file mode 100644
index 00000000..8e233a96
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/picture_classification.md
@@ -0,0 +1,17 @@
+## Figures Example
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Figure 1: This is an example image.
+
+<!-- image -->
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
+
+Figure 2: This is an example image.
+
+<!-- image -->
+
+Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/picture_classification.pages.json b/tests/data/groundtruth/docling_v2/picture_classification.pages.json
new file mode 100644
index 00000000..eb873e97
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/picture_classification.pages.json
@@ -0,0 +1 @@
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diff --git a/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md b/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md
index 2a0896cb..3fa27f96 100644
--- a/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md
+++ b/tests/data/groundtruth/docling_v2/pntd.0008301.xml.md
@@ -1,6 +1,6 @@
 # Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis
 
-Burgert-Brucker Clara R.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Zoerhoff Kathryn L.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Headland Maureen; 1: Global Health Division, RTI International, Washington, DC, United States of America, 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Shoemaker Erica A.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Stelmach Rachel; 1: Global Health Division, RTI International, Washington, DC, United States of America; Karim Mohammad Jahirul; 3: Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; Batcho Wilfrid; 4: National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Bougouma Clarisse; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Bougma Roland; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Benjamin Didier Biholong; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Georges Nko'Ayissi; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Marfo Benjamin; 7: Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Lemoine Jean Frantz; 8: Ministry of Health, Port-au-Prince, Haiti; Pangaribuan Helena Ullyartha; 9: National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Wijayanti Eksi; 9: National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Coulibaly Yaya Ibrahim; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Doumbia Salif Seriba; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Rimal Pradip; 11: Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Salissou Adamou Bacthiri; 12: Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; Bah Yukaba; 13: National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Mwingira Upendo; 14: Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; Nshala Andreas; 15: IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Muheki Edridah; 16: Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Shott Joseph; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Yevstigneyeva Violetta; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Ndayishimye Egide; 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Baker Margaret; 1: Global Health Division, RTI International, Washington, DC, United States of America; Kraemer John; 1: Global Health Division, RTI International, Washington, DC, United States of America, 18: Georgetown University, Washington, DC, United States of America; Brady Molly; 1: Global Health Division, RTI International, Washington, DC, United States of America
+Burgert-Brucker Clara R.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Zoerhoff Kathryn L.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Headland Maureen; 1: Global Health Division, RTI International, Washington, DC, United States of America, 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Shoemaker Erica A.; 1: Global Health Division, RTI International, Washington, DC, United States of America; Stelmach Rachel; 1: Global Health Division, RTI International, Washington, DC, United States of America; Karim Mohammad Jahirul; 3: Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; Batcho Wilfrid; 4: National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Bougouma Clarisse; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Bougma Roland; 5: Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Benjamin Didier Biholong; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Georges Nko'Ayissi; 6: National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Marfo Benjamin; 7: Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Lemoine Jean Frantz; 8: Ministry of Health, Port-au-Prince, Haiti; Pangaribuan Helena Ullyartha; 9: National Institute Health Research &amp; Development, Ministry of Health, Jakarta, Indonesia; Wijayanti Eksi; 9: National Institute Health Research &amp; Development, Ministry of Health, Jakarta, Indonesia; Coulibaly Yaya Ibrahim; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Doumbia Salif Seriba; 10: Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Rimal Pradip; 11: Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Salissou Adamou Bacthiri; 12: Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; Bah Yukaba; 13: National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Mwingira Upendo; 14: Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; Nshala Andreas; 15: IMA World Health/Tanzania NTD Control Programme, Uppsala University, &amp; TIBA Fellow, Dar es Salaam, Tanzania; Muheki Edridah; 16: Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Shott Joseph; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Yevstigneyeva Violetta; 17: Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Ndayishimye Egide; 2: Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Baker Margaret; 1: Global Health Division, RTI International, Washington, DC, United States of America; Kraemer John; 1: Global Health Division, RTI International, Washington, DC, United States of America, 18: Georgetown University, Washington, DC, United States of America; Brady Molly; 1: Global Health Division, RTI International, Washington, DC, United States of America
 
 ## Abstract
 
@@ -36,7 +36,7 @@ Potential covariates were derived from the available data for each factor in the
 
 #### Baseline prevalence
 
-Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.
+Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of &lt;5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of &lt;10% or ≥10%.
 
 #### Agent
 
@@ -90,9 +90,9 @@ This paper reports for the first time factors associated with pre-TAS results fr
 
 Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].
 
-Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.
+Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (&lt;350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.
 
-The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].
+The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (&gt;0.3) and higher rainfall (&gt;700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].
 
 All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.
 
@@ -110,16 +110,16 @@ Table 1: Categorization of potential factors influencing pre-TAS results.
 
 | Domain                 | Factor                | Covariate                     | Description                                                     | Reference Group      | Summary statistic   | Temporal Resolution   | Source             |
 |------------------------|-----------------------|-------------------------------|-----------------------------------------------------------------|----------------------|---------------------|-----------------------|--------------------|
-| Prevalence             | Baseline prevalence   | 5% cut off                    | Maximum reported mapping or baseline sentinel site prevalence   | <5%                  | Maximum             | Varies                | Programmatic data  |
-| Prevalence             | Baseline prevalence   | 10% cut off                   | Maximum reported mapping or baseline sentinel site prevalence   | <10%                 | Maximum             | Varies                | Programmatic data  |
-| Agent                  | Parasite              | Parasite                      | Predominate parasite in district                                | W. bancrofti & mixed | Binary value        | 2018                  | Programmatic data  |
-| Environment            | Vector                | Vector                        | Predominate vector in district                                  | Anopheles & Mansonia | Binary value        | 2018                  | Country expert     |
-| Environment            | Geography             | Elevation                     | Elevation measured in meters                                    | >350                 | Mean                | 2000                  | CGIAR-CSI SRTM [9] |
-| Environment            | Geography             | District area                 | Area measured in km2                                            | >2,500               | Maximum sum         | Static                | Programmatic data  |
-| Environment            | Climate               | EVI                           | Enhanced vegetation index                                       | > 0.3                | Mean                | 2015                  | MODIS [10]         |
+| Prevalence             | Baseline prevalence   | 5% cut off                    | Maximum reported mapping or baseline sentinel site prevalence   | &lt;5%                  | Maximum             | Varies                | Programmatic data  |
+| Prevalence             | Baseline prevalence   | 10% cut off                   | Maximum reported mapping or baseline sentinel site prevalence   | &lt;10%                 | Maximum             | Varies                | Programmatic data  |
+| Agent                  | Parasite              | Parasite                      | Predominate parasite in district                                | W. bancrofti &amp; mixed | Binary value        | 2018                  | Programmatic data  |
+| Environment            | Vector                | Vector                        | Predominate vector in district                                  | Anopheles &amp; Mansonia | Binary value        | 2018                  | Country expert     |
+| Environment            | Geography             | Elevation                     | Elevation measured in meters                                    | &gt;350                 | Mean                | 2000                  | CGIAR-CSI SRTM [9] |
+| Environment            | Geography             | District area                 | Area measured in km2                                            | &gt;2,500               | Maximum sum         | Static                | Programmatic data  |
+| Environment            | Climate               | EVI                           | Enhanced vegetation index                                       | &gt; 0.3                | Mean                | 2015                  | MODIS [10]         |
 | Environment            | Climate               | Rainfall                      | Annual rainfall measured in mm                                  | ≤ 700                | Mean                | 2015                  | CHIRPS [11]        |
 | Environment            | Socio-economic        | Population density            | Number of people per km2                                        | ≤ 100                | Mean                | 2015                  | WorldPop [12]      |
-| Environment            | Socio-economic        | Nighttime lights              | Nighttime light index from 0 to 63                              | >1.5                 | Mean                | 2015                  | VIIRS [13]         |
+| Environment            | Socio-economic        | Nighttime lights              | Nighttime light index from 0 to 63                              | &gt;1.5                 | Mean                | 2015                  | VIIRS [13]         |
 | Environment            | Co-endemicity         | Co-endemic for onchocerciasis | Part or all of district is also endemic for onchocerciases      | Non-endemic          | Binary value        | 2018                  | Programmatic data  |
 | MDA                    | Drug efficacy         | Drug package                  | DEC-ALB or IVM-ALB                                              | DEC-ALB              | Binary value        | 2018                  | Programmatic data  |
 | MDA                    | Implementation of MDA | Coverage                      | Median MDA coverage for last 5 rounds                           | ≥ 65%                | Median              | Varies                | Programmatic data  |
@@ -136,12 +136,12 @@ Table 2: Adjusted risk ratios for pre-TAS failure from log-binomial model sensit
 | Number of Failures                          | 74               | 74                         | 44                       | 72                                   | 46                              |
 | Number of total districts                   | (N = 554)        | (N = 420)                  | (N = 407)                | (N = 518)                            | (N = 414)                       |
 | Covariate                                   | RR (95% CI)      | RR (95% CI)                | RR (95% CI)              | RR (95% CI)                          | RR (95% CI)                     |
-| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92)           | 14.52 (1.79–117.82)      | 2.61 (1.03–6.61)                     | 15.80 (1.95–127.67)             |
-| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68)           | 1.00 (0.00–0.00)         | 0.88 (0.21–3.60)                     | 1.00 (0.00–0.00)                |
+| Baseline prevalence &gt; = 10% &amp; used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92)           | 14.52 (1.79–117.82)      | 2.61 (1.03–6.61)                     | 15.80 (1.95–127.67)             |
+| Baseline prevalence &gt; = 10% &amp; used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68)           | 1.00 (0.00–0.00)         | 0.88 (0.21–3.60)                     | 1.00 (0.00–0.00)                |
 | +Used FTS test                              | 1.16 (0.52–2.59) | 2.40 (1.12–5.11)           | 0.15 (0.02–1.11)         | 1.03 (0.45–2.36)                     | 0.13 (0.02–0.96)                |
 | +Used ICT test                              | 0.92 (0.32–2.67) | 1.47 (0.51–4.21)           | 0.33 (0.04–2.54)         | 0.82 (0.28–2.43)                     | 0.27 (0.03–2.04)                |
-| +Baseline prevalence > = 10%                | 2.52 (1.37–4.64) | 2.42 (1.31–4.47)           | 2.03 (1.06–3.90)         | 2.30 (1.21–4.36)                     | 2.01 (1.07–3.77)                |
-| Elevation < 350m                            | 3.07 (1.95–4.83) | 2.21 (1.42–3.43)           | 4.68 (2.22–9.87)         | 3.04 (1.93–4.79)                     | 3.76 (1.92–7.37)                |
+| +Baseline prevalence &gt; = 10%                | 2.52 (1.37–4.64) | 2.42 (1.31–4.47)           | 2.03 (1.06–3.90)         | 2.30 (1.21–4.36)                     | 2.01 (1.07–3.77)                |
+| Elevation &lt; 350m                            | 3.07 (1.95–4.83) | 2.21 (1.42–3.43)           | 4.68 (2.22–9.87)         | 3.04 (1.93–4.79)                     | 3.76 (1.92–7.37)                |
 
 ## Figures
 
diff --git a/tests/data/groundtruth/docling_v2/pone.0234687.xml.md b/tests/data/groundtruth/docling_v2/pone.0234687.xml.md
index 0e9c0f02..36758fd5 100644
--- a/tests/data/groundtruth/docling_v2/pone.0234687.xml.md
+++ b/tests/data/groundtruth/docling_v2/pone.0234687.xml.md
@@ -62,7 +62,7 @@ The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a funct
 
 The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
 
-The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
+The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (&gt; 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
 
 The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
 
@@ -106,7 +106,7 @@ The lower C footprint in scenarios with access to pasture, when local emission f
 
 The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
 
-The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
+The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (&lt;4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
 
 ### Emissions from excreta and feed production
 
diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.json
index 6e04cad7..5d884b0a 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.json
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.json
@@ -1 +1 @@
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{"self_ref": "#/texts/34", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 251.60325622558594, "r": 466.77880859375, "b": 244.1992645263672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "r Extremely large database and overcoming limits to growth", "text": "r Extremely large database and overcoming limits to growth", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 241.42054748535156, "r": 382.2095642089844, "b": 234.0165557861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "r ISV education and enablement", "text": "r ISV education and enablement", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH<SM590000> Security fundamentals", "text": "GLYPH<SM590000> Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH<SM590000> Current state of IBM i security", "text": "GLYPH<SM590000> Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH<SM590000> DB2 for i security controls", "text": "GLYPH<SM590000> DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.86685, "t": 652.32031, "r": 257.58578, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": 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610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.30872, "t": 610.93744, "r": 226.86777, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.86548000000002, "t": 610.93744, "r": 232.58441, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": 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"orig": "DISABLE", "text": "DISABLE"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.58514, "t": 404.0228, "r": 190.6628, "b": 394.20416000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.73431, "t": 415.34283, "r": 455.83047000000005, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Specifies that the row permission is to", "text": "Specifies that the row permission is to"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8848, "t": 415.34283, "r": 527.62122, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "be initially disabled", "text": "be initially disabled"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "USER = ALICE CURRENT USER = ALICE", "text": "USER = ALICE CURRENT USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/226", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/15"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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"coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5494995117188, "t": 564.3729248046875, "r": 547.1978759765625, "b": 555.159912109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 551.89306640625, "r": 284.0286560058594, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449462890625, "t": 551.89306640625, "r": 547.1211547851562, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 536.0958862304688, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6658935546875, "t": 432.8550720214844, "r": 547.2047119140625, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 420.37518310546875, "r": 535.9526977539062, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.5558471679688, "t": 420.37518310546875, "r": 547.1590576171875, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, 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{"bbox": {"l": 151.19720458984375, "t": 395.35565185546875, "r": 536.0748901367188, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6302490234375, "t": 395.35565185546875, "r": 547.1856079101562, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 382.8757629394531, "r": 411.2704772949219, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.8177490234375, "t": 382.8757629394531, "r": 547.1786499023438, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 370.3958740234375, "r": 536.035888671875, "b": 361.1828918457031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", 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"coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 345.3763427734375, "r": 530.5352172851562, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755004882812, "t": 345.3763427734375, "r": 547.156005859375, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 332.8964538574219, "r": 547.256591796875, "b": 323.6834716796875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 310.3968200683594, "r": 530.5396118164062, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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"end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1971893310547, "t": 273.3774719238281, "r": 530.4347534179688, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.9962158203125, "t": 273.3774719238281, "r": 547.1190795898438, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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{"self_ref": "#/texts/34", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 251.60325622558594, "r": 466.77880859375, "b": 244.1992645263672, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "r Extremely large database and overcoming limits to growth", "text": "r Extremely large database and overcoming limits to growth", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/groups/1"}, "children": [], "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.2401123046875, "t": 241.42054748535156, "r": 382.2095642089844, "b": 234.0165557861328, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "r ISV education and enablement", "text": "r ISV education and enablement", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 718.1519775390625, "r": 151.46160888671875, "b": 695.9519653320312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79983520507812, "t": 659.3513793945312, "r": 547.3082275390625, "b": 590.1392822265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.79986572265625, "t": 577.3925170898438, "r": 546.4656982421875, "b": 532.1800537109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8000030517578, "t": 471.37127685546875, "r": 547.2366943359375, "b": 450.1584777832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.8599853515625, "t": 37.15127944946289, "r": 547.2503051757812, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.3995666503906, "t": 416.3512268066406, "r": 541.2507934570312, "b": 275.1402587890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.39959716796875, "t": 264.37347412109375, "r": 541.2737426757812, "b": 111.162841796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.80000305175781, "t": 503.69940185546875, "r": 125.36660766601562, "b": 488.9364013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457275390625, "r": 115.13253021240234, "b": 517.019287109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1."}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.3999938964844, "t": 698.831298828125, "r": 522.6177368164062, "b": 661.8682861328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 537.1136474609375, "r": 547.3047485351562, "b": 482.1217956542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.79965209960938, "t": 443.2912902832031, "r": 547.2540283203125, "b": 362.078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 648]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.80023193359375, "t": 349.27227783203125, "r": 527.206298828125, "b": 304.0598449707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 291.3130187988281, "r": 547.1551513671875, "b": 270.1002197265625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 262.2736511230469, "r": 250.23167419433594, "b": 253.06063842773438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "GLYPH<SM590000> Security fundamentals", "text": "GLYPH<SM590000> Security fundamentals", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 250.27383422851562, "r": 282.98114013671875, "b": 241.0608367919922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "GLYPH<SM590000> Current state of IBM i security", "text": "GLYPH<SM590000> Current state of IBM i security", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8002471923828, "t": 238.27403259277344, "r": 264.8818664550781, "b": 229.06103515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "GLYPH<SM590000> DB2 for i security controls", "text": "GLYPH<SM590000> DB2 for i security controls", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 74.24993896484375, "r": 258.362548828125, "b": 67.21955871582031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "$^{1 }$http://www.idtheftcenter.org", "text": "$^{1 }$http://www.idtheftcenter.org"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8000030517578, "t": 64.40973663330078, "r": 234.05880737304688, "b": 57.02824020385742, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "$^{2 }$http://www.ponemon.org /", "text": "$^{2 }$http://www.ponemon.org /"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.80000305175781, "t": 36.461997985839844, "r": 257.24334716796875, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.6798706054688, "t": 37.15127944946289, "r": 547.2176513671875, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 717.6593017578125, "r": 267.40582275390625, "b": 702.8963012695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 685.3912963867188, "r": 545.0048217773438, "b": 664.178466796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 656.8751220703125, "r": 547.1642456054688, "b": 611.138916015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 361]}], "orig": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 603.3721313476562, "r": 547.2608642578125, "b": 522.1602172851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199462890625, "t": 514.3934326171875, "r": 541.9920043945312, "b": 505.180419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.79930114746094, "t": 497.8750305175781, "r": 547.1582641601562, "b": 416.139404296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8002166748047, "t": 403.392578125, "r": 535.3616943359375, "b": 382.1797790527344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 353.69927978515625, "r": 323.3839111328125, "b": 338.936279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 321.37127685546875, "r": 547.3182373046875, "b": 276.1588439941406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 263.3522644042969, "r": 547.284423828125, "b": 206.1400604248047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8000030517578, "t": 193.33349609375, "r": 547.2832641601562, "b": 112.12167358398438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 72.8219985961914, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84030151367188, "t": 36.461997985839844, "r": 328.7253723144531, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 544.3033447265625, "b": 639.2794189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.80000305175781, "t": 618.665283203125, "r": 301.4690246582031, "b": 606.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 592.5112915039062, "r": 541.5673828125, "b": 535.2990112304688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 522.492431640625, "r": 547.4407958984375, "b": 477.27996826171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.79998779296875, "t": 464.473388671875, "r": 547.232666015625, "b": 431.2607727050781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8000030517578, "t": 100.18199920654297, "r": 316.447265625, "b": 91.85700225830078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.95911, "t": 408.54388, "r": 209.08017, "b": 402.9216, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/8"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.00624, "t": 401.04749, "r": 220.10355, "b": 395.42519999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 720.665283203125, "r": 335.4955139160156, "b": 708.67724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 694.5112915039062, "r": 547.284423828125, "b": 685.2982788085938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 677.4717407226562, "r": 301.5174865722656, "b": 668.2587280273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.80099487304688, "t": 665.471923828125, "r": 313.39776611328125, "b": 656.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8009796142578, "t": 653.4721069335938, "r": 310.8171081542969, "b": 644.2590942382812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.7999725341797, "t": 631.5123291015625, "r": 512.5380249023438, "b": 610.2994995117188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.80096435546875, "t": 602.3235473632812, "r": 441.59686279296875, "b": 593.5487670898438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 572.6453247070312, "r": 544.4754638671875, "b": 560.6572875976562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 546.4913330078125, "r": 519.5179443359375, "b": 525.2785034179688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 512.4420166015625, "r": 283.9680480957031, "b": 504.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 339.49127197265625, "r": 547.2803955078125, "b": 318.2784729003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 305.4420166015625, "r": 462.35418701171875, "b": 297.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.34198, "r": 171.26956, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "SELECT", "text": "SELECT"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 182.75941, "t": 288.34198, "r": 251.69853, "b": 279.56719999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "function_id,", "text": "function_id,"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 166.78244, "t": 276.3421599999999, "r": 241.73852999999997, "b": 267.56737999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 170.75961, "t": 264.34235, "r": 221.69901999999996, "b": 255.56758000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 167.53809, "t": 252.34253, "r": 236.69878, "b": 243.56777999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.34272999999996, "r": 160.59396, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "FROM", "text": "FROM"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 178.43944, "t": 240.34272999999996, "r": 261.71829, "b": 231.56798000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "function_usage", "text": "function_usage"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.34293000000002, "r": 162.44176, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "WHERE", "text": "WHERE"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 177.8268, "t": 228.34293000000002, "r": 331.67731, "b": 219.56817999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "function_id=\u2019QIBM_DB_SECADM\u2019", "text": "function_id=\u2019QIBM_DB_SECADM\u2019"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.34312, "r": 178.77542, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ORDER BY", "text": "ORDER BY"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/6"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 189.26929, "t": 216.34312, "r": 241.73856, "b": 207.56836999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name;", "text": "user_name;"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 171.7793731689453, "r": 249.59605407714844, "b": 157.01637268066406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8000030517578, "t": 139.45127868652344, "r": 547.2234497070312, "b": 82.23904418945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 542.6943359375, "b": 651.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 638.4722900390625, "r": 547.303955078125, "b": 593.2598266601562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 580.5130615234375, "r": 538.6507568359375, "b": 559.3002319335938, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 546.49365234375, "r": 545.7960205078125, "b": 513.281005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 500.47442626953125, "r": 539.80712890625, "b": 455.2619934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.79959106445312, "t": 442.5151672363281, "r": 543.067138671875, "b": 421.3023681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.80000305175781, "t": 408.4620056152344, "r": 391.754638671875, "b": 400.1369934082031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32000732421875, "t": 36.461997985839844, "r": 523.5407104492188, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.799560546875, "t": 720.490966796875, "r": 528.7305908203125, "b": 699.2781372070312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 377.86199951171875, "r": 341.9765930175781, "b": 369.5369873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.1337, "t": 652.32031, "r": 246.7961, "b": 642.49017, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.86685, "t": 652.32031, "r": 257.58578, "b": 642.50165, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": 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610.93744, "r": 163.45079, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.58405, "t": 610.93744, "r": 174.30298, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.30872, "t": 610.93744, "r": 226.86777, "b": 601.1073, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.86548000000002, "t": 610.93744, "r": 232.58441, "b": 601.11877, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">"}, {"self_ref": 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"orig": "DISABLE", "text": "DISABLE"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.58514, "t": 404.0228, "r": 190.6628, "b": 394.20416000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.73431, "t": 415.34283, "r": 455.83047000000005, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Specifies that the row permission is to", "text": "Specifies that the row permission is to"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/9"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 455.8848, "t": 415.34283, "r": 527.62122, "b": 407.47025, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "be initially disabled", "text": "be initially disabled"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 352.0559997558594, "r": 215.37600708007812, "b": 340.95599365234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8000030517578, "t": 336.9112854003906, "r": 542.7664794921875, "b": 291.6988525390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 11, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 412.20758056640625, "b": 711.2779541015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 698.501953125, "r": 372.6036376953125, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 577.5112915039062, "r": 538.493896484375, "b": 556.2984619140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"self_ref": "#/texts/174", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 548.471923828125, "r": 411.36138916015625, "b": 539.2589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 531.4921264648438, "r": 453.2580871582031, "b": 522.2791137695312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 514.5123291015625, "r": 541.4498291015625, "b": 493.29949951171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 485.472900390625, "r": 547.2167358398438, "b": 452.2602844238281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 253]}], "orig": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/groups/7"}, "children": [], "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.80101013183594, "t": 444.49346923828125, "r": 547.3540649414062, "b": 423.2806701660156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 195.2821044921875, "r": 341.2566223144531, "b": 186.95709228515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/180", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.7323, "t": 405.01547, "r": 218.71170000000004, "b": 396.50473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "SignedonasALICE Signed on as ALICE", "text": "SignedonasALICE Signed on as ALICE"}, {"self_ref": "#/texts/181", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 381.12558000000007, "r": 191.70256, "b": 372.61484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/182", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 369.18066, "r": 232.56117, "b": 360.66992, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/183", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 345.29076999999995, "r": 183.26944, "b": 336.78003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 318.41476, "r": 184.17328, "b": 309.90402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "P1 Proc1:", "text": "P1 Proc1:"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 306.46985, "r": 209.103, "b": 297.95911, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.52185, "t": 294.52493, "r": 281.68927, "b": 286.01419, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 270.63507000000004, "r": 201.65666, "b": 262.12433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.4301, "t": 258.69016, "r": 234.57686999999999, "b": 250.17940999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/189", "parent": {"cref": "#/pictures/10"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.84158000000002, "r": 232.56117, "b": 205.38590999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "USER = ALICE CURRENT USER = ALICE", "text": "USER = ALICE CURRENT USER = ALICE"}, {"self_ref": "#/texts/190", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.80000305175781, "t": 166.44528198242188, "r": 247.02536010742188, "b": 154.457275390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 140.29127502441406, "r": 518.0011596679688, "b": 119.0784683227539, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8000030517578, "t": 106.27189636230469, "r": 532.3385009765625, "b": 73.05928039550781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"self_ref": "#/texts/193", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 342.5477294921875, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/198", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 698.501953125, "r": 201.1814727783203, "b": 690.177001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.80000305175781, "t": 469.7992858886719, "r": 384.3638916015625, "b": 455.0362854003906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/200", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8000030517578, "t": 437.4712829589844, "r": 547.2347412109375, "b": 356.2593994140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 343.5125732421875, "r": 547.2573852539062, "b": 310.2999572753906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 297.4933776855469, "r": 458.44525146484375, "b": 288.2803955078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/203", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 280.45379638671875, "r": 406.0775146484375, "b": 271.2408142089844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 263.4739990234375, "r": 396.9881591796875, "b": 254.26100158691406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/205", "parent": {"cref": "#/groups/8"}, "children": [], "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.80001831054688, "t": 246.4941864013672, "r": 536.568603515625, "b": 225.28138732910156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/206", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.20018005371094, "t": 217.305419921875, "r": 451.01605224609375, "b": 150.57144165039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "code_language": "unknown"}, {"self_ref": "#/texts/207", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 720.341552734375, "r": 166.73934936523438, "b": 711.5667724609375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 708.3417358398438, "r": 156.7793426513672, "b": 699.5669555664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/209", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 696.3419189453125, "r": 521.5742797851562, "b": 531.5695190429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 437]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "code_language": "unknown"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.79959106445312, "t": 516.4940795898438, "r": 547.2122192382812, "b": 495.2812805175781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 487.51446533203125, "r": 469.1528015136719, "b": 478.3014831542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/212", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 470.4748840332031, "r": 403.95953369140625, "b": 461.26190185546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/213", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 453.4950866699219, "r": 545.16845703125, "b": 432.28228759765625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.03939819335938, "t": 424.5154724121094, "r": 529.463623046875, "b": 415.302490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/groups/9"}, "children": [], "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.1997833251953, "t": 407.47589111328125, "r": 530.060302734375, "b": 398.2629089355469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/216", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 385.48199462890625, "r": 351.9873046875, "b": 377.156982421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8000030517578, "t": 368.3218994140625, "r": 526.5546875, "b": 107.55116271972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 590]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "code_language": "unknown"}, {"self_ref": "#/texts/218", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94000244140625, "t": 36.461997985839844, "r": 523.6016235351562, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/220", "parent": {"cref": "#/groups/10"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 720.4913330078125, "r": 449.952392578125, "b": 711.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 618.4619750976562, "r": 293.1380920410156, "b": 610.1370239257812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 589.6253051757812, "r": 203.98521423339844, "b": 577.6372680664062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 563.4713134765625, "r": 547.2256469726562, "b": 530.2586669921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"self_ref": "#/texts/224", "parent": {"cref": "#/groups/11"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 522.4918823242188, "r": 409.4788818359375, "b": 513.2788696289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 500.4420166015625, "r": 375.2909851074219, "b": 492.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/226", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 483.3418884277344, "r": 376.6766052246094, "b": 474.5671081542969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/227", "parent": {"cref": "#/groups/12"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 471.3420715332031, "r": 354.86962890625, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "/* Active Column Access Control (masks)", "text": "/* Active Column Access Control (masks)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 365.77313232421875, "t": 471.3420715332031, "r": 376.6766052246094, "b": 462.5672912597656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "*/", "text": "*/"}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 459.3422546386719, "r": 291.7178039550781, "b": 450.5674743652344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 447.3424377441406, "r": 271.6783142089844, "b": 438.5676574707031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL"}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 435.3426208496094, "r": 291.7178039550781, "b": 426.5678405761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/232", "parent": {"cref": "#/groups/13"}, "children": [], "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8000030517578, "t": 411.4924011230469, "r": 540.8014526367188, "b": 378.27978515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 142.9621124267578, "r": 347.4305419921875, "b": 134.63710021972656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 78.4020004272461, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42030334472656, "t": 36.461997985839844, "r": 334.4214172363281, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/236", "parent": {"cref": "#/groups/14"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.79959106445312, "t": 720.490966796875, "r": 514.048583984375, "b": 687.2783203125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 311.4420166015625, "r": 327.0932922363281, "b": 303.11700439453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/238", "parent": {"cref": "#/groups/15"}, "children": [], "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8000030517578, "t": 285.4313659667969, "r": 547.2394409179688, "b": 252.21875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.80000305175781, "t": 124.48210144042969, "r": 227.1014862060547, "b": 116.15709686279297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8000030517578, "t": 36.461997985839844, "r": 523.5935668945312, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.0999755859375, "t": 37.15127944946289, "r": 547.2591552734375, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.80030822753906, "t": 720.3270263671875, "r": 500.697265625, "b": 85.39237976074219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1998]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "code_language": "unknown"}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.80000305175781, "t": 37.15127944946289, "r": 83.98200225830078, "b": 27.93828010559082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124"}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94000244140625, "t": 36.461997985839844, "r": 339.819580078125, "b": 28.136999130249023, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.2200012207031, "t": 763.4519653320312, "r": 414.24481201171875, "b": 741.251953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover"}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.3619995117188, "r": 447.3600158691406, "b": 651.5399780273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "level": 1}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 549.8280029296875, "r": 127.443603515625, "b": 525.1680297851562, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties"}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 507.8280334472656, "r": 120.283203125, "b": 469.1280212402344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database"}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.700000762939453, "t": 451.8480224609375, "r": 121.44960021972656, "b": 413.14801025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Protect columns by defining column masks", "text": "Protect columns by defining column masks"}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94000244140625, "t": 549.2714233398438, "r": 414.084228515625, "b": 468.4081115722656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.9400177001953, "t": 460.292724609375, "r": 414.173828125, "b": 403.4290466308594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "label": "page_footer", "prov": [{"page_no": 18, "bbox": {"l": 171.0, "t": 160.66200256347656, "r": 231.8876953125, "b": 152.3369903564453, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "REDP-5110-00", "text": "REDP-5110-00"}, {"self_ref": "#/texts/253", "parent": {"cref": "#/pictures/15"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 558.11987, "t": 746.5313100000001, "r": 565.46039, "b": 737.3183, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "fi", "text": "fi"}, {"self_ref": "#/texts/254", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 474.60001, "t": 627.94342, "r": 580.88989, "b": 603.05902, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Redpaper", "text": "Redpaper"}, {"self_ref": "#/texts/255", "parent": {"cref": "#/pictures/16"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 582.53992, "t": 619.67285, "r": 592.13989, "b": 610.79285, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2122", "text": "\u2122"}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 544.2816772460938, "r": 559.809326171875, "b": 489.8393859863281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION"}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 440.2080078125, "r": 587.38916015625, "b": 405.52801513671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE"}, {"self_ref": "#/texts/258", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 467.3399963378906, "t": 392.13970947265625, "r": 587.5205078125, "b": 250.36593627929688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 323]}], "orig": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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"row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5494995117188, "t": 564.3729248046875, "r": 547.1978759765625, "b": 555.159912109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 551.89306640625, "r": 284.0286560058594, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449462890625, "t": 551.89306640625, "r": 547.1211547851562, "b": 542.6800537109375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 529.3934326171875, "r": 536.0958862304688, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6658935546875, "t": 432.8550720214844, "r": 547.2047119140625, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 420.37518310546875, "r": 535.9526977539062, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.5558471679688, "t": 420.37518310546875, "r": 547.1590576171875, "b": 411.1622009277344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, 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{"bbox": {"l": 151.19720458984375, "t": 395.35565185546875, "r": 536.0748901367188, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6302490234375, "t": 395.35565185546875, "r": 547.1856079101562, "b": 386.1426696777344, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 382.8757629394531, "r": 411.2704772949219, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.8177490234375, "t": 382.8757629394531, "r": 547.1786499023438, "b": 373.66278076171875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 370.3958740234375, "r": 536.035888671875, "b": 361.1828918457031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", 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"coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19720458984375, "t": 345.3763427734375, "r": 530.5352172851562, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755004882812, "t": 345.3763427734375, "r": 547.156005859375, "b": 336.1633605957031, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.7970428466797, "t": 332.8964538574219, "r": 547.256591796875, "b": 323.6834716796875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 310.3968200683594, "r": 530.5396118164062, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0916748046875, "t": 310.3968200683594, "r": 547.19580078125, "b": 301.183837890625, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79702758789062, "t": 298.3970031738281, "r": 530.4808959960938, "b": 289.18402099609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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"end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1971893310547, "t": 273.3774719238281, "r": 530.4347534179688, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.9962158203125, "t": 273.3774719238281, "r": 547.1190795898438, "b": 264.16448974609375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79901123046875, "t": 646.8715209960938, "r": 189.86537170410156, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 195.3968505859375, "t": 646.8715209960938, "r": 547.182861328125, "b": 637.6585083007812, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, 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"Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79803466796875, "t": 576.852783203125, "r": 339.18292236328125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.714111328125, "t": 576.852783203125, "r": 547.1387939453125, "b": 567.6397705078125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, 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Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6468505859375, "t": 529.3934326171875, "r": 547.1978149414062, "b": 520.180419921875, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79808044433594, "t": 517.3936157226562, "r": 549.8472290039062, "b": 508.18060302734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 504.85394287109375, "r": 536.1293334960938, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6611328125, "t": 504.85394287109375, "r": 547.19287109375, "b": 495.6409606933594, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.79806518554688, "t": 492.3740539550781, "r": 549.8472290039062, "b": 483.16107177734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": null, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 479.8941650390625, "r": 536.0551147460938, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015014648438, "t": 479.8941650390625, "r": 547.14794921875, "b": 470.6811828613281, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 151.19720458984375, "t": 467.3545227050781, "r": 536.080078125, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.635498046875, "t": 467.3545227050781, "r": 547.19091796875, "b": 458.14154052734375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 444.8548889160156, "r": 536.0908813476562, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.642822265625, "t": 444.8548889160156, "r": 547.1947631835938, "b": 435.64190673828125, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 136.7970428466797, "t": 432.8550720214844, "r": 536.1271362304688, "b": 423.64208984375, "coord_origin": "BOTTOMLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, 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diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.md b/tests/data/groundtruth/docling_v2/redp5110_sampled.md
index a0e71aad..460b7a35 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.md
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.md
@@ -63,10 +63,10 @@ Solution Brief IBM Systems Lab Services and Training
 
 ## Highlights
 
-- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g81>GLYPH<g75>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g72>GLYPH<g3> GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g73>GLYPH<g82>GLYPH<g85>GLYPH<g80>GLYPH<g68>GLYPH<g81>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g92>GLYPH<g82>GLYPH<g88>GLYPH<g85> GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>
-- GLYPH<g115>GLYPH<g3> GLYPH<g40>GLYPH<g68>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g74>GLYPH<g85>GLYPH<g72>GLYPH<g68>GLYPH<g87>GLYPH<g72>GLYPH<g85>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g87>GLYPH<g88>GLYPH<g85> GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g55>GLYPH<g3> GLYPH<g83>GLYPH<g85>GLYPH<g82>GLYPH<g77>GLYPH<g72>GLYPH<g70>GLYPH<g87>GLYPH<g86> GLYPH<g3> GLYPH<g87>GLYPH<g75>GLYPH<g85>GLYPH<g82>GLYPH<g88>GLYPH<g74>GLYPH<g75>GLYPH<g3> GLYPH<g80>GLYPH<g82>GLYPH<g71>GLYPH<g72>GLYPH<g85> GLYPH<g81>GLYPH<g76>GLYPH<g93>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g71>GLYPH<g68>GLYPH<g87>GLYPH<g68>GLYPH<g69>GLYPH<g68>GLYPH<g86>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71> GLYPH<g3> GLYPH<g68>GLYPH<g83>GLYPH<g83>GLYPH<g79>GLYPH<g76>GLYPH<g70>GLYPH<g68>GLYPH<g87>GLYPH<g76>GLYPH<g82>GLYPH<g81>GLYPH<g86>
-- GLYPH<g115>GLYPH<g3> GLYPH<g53>GLYPH<g72>GLYPH<g79>GLYPH<g92>GLYPH<g3> GLYPH<g82>GLYPH<g81>GLYPH<g3> GLYPH<g44>GLYPH<g37>GLYPH<g48>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g3> GLYPH<g70>GLYPH<g82>GLYPH<g81>GLYPH<g86>GLYPH<g88>GLYPH<g79>GLYPH<g87>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g15>GLYPH<g3> GLYPH<g86>GLYPH<g78>GLYPH<g76>GLYPH<g79>GLYPH<g79>GLYPH<g86> GLYPH<g3> GLYPH<g86>GLYPH<g75>GLYPH<g68>GLYPH<g85>GLYPH<g76>GLYPH<g81>GLYPH<g74>GLYPH<g3> GLYPH<g68>GLYPH<g81>GLYPH<g71>GLYPH<g3> GLYPH<g85>GLYPH<g72>GLYPH<g81>GLYPH<g82>GLYPH<g90>GLYPH<g81>GLYPH<g3> GLYPH<g86>GLYPH<g72>GLYPH<g85>GLYPH<g89>GLYPH<g76>GLYPH<g70>GLYPH<g72>GLYPH<g86>
-- GLYPH<g115>GLYPH<g3> GLYPH<g55> GLYPH<g68>GLYPH<g78>GLYPH<g72>GLYPH<g3> GLYPH<g68>GLYPH<g71>GLYPH<g89>GLYPH<g68>GLYPH<g81>GLYPH<g87>GLYPH<g68>GLYPH<g74>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g68>GLYPH<g70>GLYPH<g70>GLYPH<g72>GLYPH<g86>GLYPH<g86>GLYPH<g3> GLYPH<g87>GLYPH<g82>GLYPH<g3> GLYPH<g68> GLYPH<g3> GLYPH<g90>GLYPH<g82>GLYPH<g85>GLYPH<g79>GLYPH<g71>GLYPH<g90>GLYPH<g76>GLYPH<g71>GLYPH<g72>GLYPH<g3> GLYPH<g86>GLYPH<g82>GLYPH<g88>GLYPH<g85>GLYPH<g70>GLYPH<g72>GLYPH<g3> GLYPH<g82>GLYPH<g73>GLYPH<g3> GLYPH<g72>GLYPH<g91>GLYPH<g83>GLYPH<g72>GLYPH<g85>GLYPH<g87>GLYPH<g76>GLYPH<g86>GLYPH<g72>
+- GLYPH&lt;g115&gt;GLYPH&lt;g3&gt; GLYPH&lt;g40&gt;GLYPH&lt;g81&gt;GLYPH&lt;g75&gt;GLYPH&lt;g68&gt;GLYPH&lt;g81&gt;GLYPH&lt;g70&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g87&gt;GLYPH&lt;g75&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g83&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g73&gt;GLYPH&lt;g82&gt;GLYPH&lt;g85&gt;GLYPH&lt;g80&gt;GLYPH&lt;g68&gt;GLYPH&lt;g81&gt;GLYPH&lt;g70&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g73&gt;GLYPH&lt;g3&gt; GLYPH&lt;g92&gt;GLYPH&lt;g82&gt;GLYPH&lt;g88&gt;GLYPH&lt;g85&gt; GLYPH&lt;g3&gt; GLYPH&lt;g71&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g68&gt;GLYPH&lt;g69&gt;GLYPH&lt;g68&gt;GLYPH&lt;g86&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g83&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g76&gt;GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g86&gt;
+- GLYPH&lt;g115&gt;GLYPH&lt;g3&gt; GLYPH&lt;g40&gt;GLYPH&lt;g68&gt;GLYPH&lt;g85&gt; GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g74&gt;GLYPH&lt;g85&gt;GLYPH&lt;g72&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g3&gt; GLYPH&lt;g85&gt;GLYPH&lt;g72&gt;GLYPH&lt;g87&gt;GLYPH&lt;g88&gt;GLYPH&lt;g85&gt; GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g44&gt;GLYPH&lt;g55&gt;GLYPH&lt;g3&gt; GLYPH&lt;g83&gt;GLYPH&lt;g85&gt;GLYPH&lt;g82&gt;GLYPH&lt;g77&gt;GLYPH&lt;g72&gt;GLYPH&lt;g70&gt;GLYPH&lt;g87&gt;GLYPH&lt;g86&gt; GLYPH&lt;g3&gt; GLYPH&lt;g87&gt;GLYPH&lt;g75&gt;GLYPH&lt;g85&gt;GLYPH&lt;g82&gt;GLYPH&lt;g88&gt;GLYPH&lt;g74&gt;GLYPH&lt;g75&gt;GLYPH&lt;g3&gt; GLYPH&lt;g80&gt;GLYPH&lt;g82&gt;GLYPH&lt;g71&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt; GLYPH&lt;g81&gt;GLYPH&lt;g76&gt;GLYPH&lt;g93&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g76&gt;GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g73&gt;GLYPH&lt;g3&gt; GLYPH&lt;g71&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g68&gt;GLYPH&lt;g69&gt;GLYPH&lt;g68&gt;GLYPH&lt;g86&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g68&gt;GLYPH&lt;g81&gt;GLYPH&lt;g71&gt; GLYPH&lt;g3&gt; GLYPH&lt;g68&gt;GLYPH&lt;g83&gt;GLYPH&lt;g83&gt;GLYPH&lt;g79&gt;GLYPH&lt;g76&gt;GLYPH&lt;g70&gt;GLYPH&lt;g68&gt;GLYPH&lt;g87&gt;GLYPH&lt;g76&gt;GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g86&gt;
+- GLYPH&lt;g115&gt;GLYPH&lt;g3&gt; GLYPH&lt;g53&gt;GLYPH&lt;g72&gt;GLYPH&lt;g79&gt;GLYPH&lt;g92&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g44&gt;GLYPH&lt;g37&gt;GLYPH&lt;g48&gt;GLYPH&lt;g3&gt; GLYPH&lt;g72&gt;GLYPH&lt;g91&gt;GLYPH&lt;g83&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g87&gt;GLYPH&lt;g3&gt; GLYPH&lt;g70&gt;GLYPH&lt;g82&gt;GLYPH&lt;g81&gt;GLYPH&lt;g86&gt;GLYPH&lt;g88&gt;GLYPH&lt;g79&gt;GLYPH&lt;g87&gt;GLYPH&lt;g76&gt;GLYPH&lt;g81&gt;GLYPH&lt;g74&gt;GLYPH&lt;g15&gt;GLYPH&lt;g3&gt; GLYPH&lt;g86&gt;GLYPH&lt;g78&gt;GLYPH&lt;g76&gt;GLYPH&lt;g79&gt;GLYPH&lt;g79&gt;GLYPH&lt;g86&gt; GLYPH&lt;g3&gt; GLYPH&lt;g86&gt;GLYPH&lt;g75&gt;GLYPH&lt;g68&gt;GLYPH&lt;g85&gt;GLYPH&lt;g76&gt;GLYPH&lt;g81&gt;GLYPH&lt;g74&gt;GLYPH&lt;g3&gt; GLYPH&lt;g68&gt;GLYPH&lt;g81&gt;GLYPH&lt;g71&gt;GLYPH&lt;g3&gt; GLYPH&lt;g85&gt;GLYPH&lt;g72&gt;GLYPH&lt;g81&gt;GLYPH&lt;g82&gt;GLYPH&lt;g90&gt;GLYPH&lt;g81&gt;GLYPH&lt;g3&gt; GLYPH&lt;g86&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g89&gt;GLYPH&lt;g76&gt;GLYPH&lt;g70&gt;GLYPH&lt;g72&gt;GLYPH&lt;g86&gt;
+- GLYPH&lt;g115&gt;GLYPH&lt;g3&gt; GLYPH&lt;g55&gt; GLYPH&lt;g68&gt;GLYPH&lt;g78&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g68&gt;GLYPH&lt;g71&gt;GLYPH&lt;g89&gt;GLYPH&lt;g68&gt;GLYPH&lt;g81&gt;GLYPH&lt;g87&gt;GLYPH&lt;g68&gt;GLYPH&lt;g74&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g73&gt;GLYPH&lt;g3&gt; GLYPH&lt;g68&gt;GLYPH&lt;g70&gt;GLYPH&lt;g70&gt;GLYPH&lt;g72&gt;GLYPH&lt;g86&gt;GLYPH&lt;g86&gt;GLYPH&lt;g3&gt; GLYPH&lt;g87&gt;GLYPH&lt;g82&gt;GLYPH&lt;g3&gt; GLYPH&lt;g68&gt; GLYPH&lt;g3&gt; GLYPH&lt;g90&gt;GLYPH&lt;g82&gt;GLYPH&lt;g85&gt;GLYPH&lt;g79&gt;GLYPH&lt;g71&gt;GLYPH&lt;g90&gt;GLYPH&lt;g76&gt;GLYPH&lt;g71&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g86&gt;GLYPH&lt;g82&gt;GLYPH&lt;g88&gt;GLYPH&lt;g85&gt;GLYPH&lt;g70&gt;GLYPH&lt;g72&gt;GLYPH&lt;g3&gt; GLYPH&lt;g82&gt;GLYPH&lt;g73&gt;GLYPH&lt;g3&gt; GLYPH&lt;g72&gt;GLYPH&lt;g91&gt;GLYPH&lt;g83&gt;GLYPH&lt;g72&gt;GLYPH&lt;g85&gt;GLYPH&lt;g87&gt;GLYPH&lt;g76&gt;GLYPH&lt;g86&gt;GLYPH&lt;g72&gt;
 
 <!-- image -->
 
@@ -130,20 +130,20 @@ Businesses must make a serious effort to secure their data and recognize that se
 
 This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:
 
-- GLYPH<SM590000> Security fundamentals
-- GLYPH<SM590000> Current state of IBM i security
-- GLYPH<SM590000> DB2 for i security controls
+- GLYPH&lt;SM590000&gt; Security fundamentals
+- GLYPH&lt;SM590000&gt; Current state of IBM i security
+- GLYPH&lt;SM590000&gt; DB2 for i security controls
 
 ## 1.1 Security fundamentals
 
 Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:
 
-- GLYPH<SM590000> First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.
+- GLYPH&lt;SM590000&gt; First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.
 - The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.
 
 A security policy is what defines whether the system and its settings are secure (or not).
 
-- GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.
+- GLYPH&lt;SM590000&gt; The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.
 
 With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.
 
@@ -173,9 +173,9 @@ Figure 1-2 Existing row and column controls
 
 The following CL commands can be used to work with, display, or change function usage IDs:
 
-- GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )
-- GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )
-- GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )
+- GLYPH&lt;SM590000&gt; Work Function Usage ( WRKFCNUSG )
+- GLYPH&lt;SM590000&gt; Change Function Usage ( CHGFCNUSG )
+- GLYPH&lt;SM590000&gt; Display Function Usage ( DSPFCNUSG )
 
 For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:
 
@@ -191,8 +191,8 @@ Table 2-1 FUNCTION\_USAGE view
 |---------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | FUNCTION\_ID   | VARCHAR(30) | ID of the function.                                                                                                                                                   |
 | USER\_NAME     | VARCHAR(10) | Name of the user profile that has a usage setting for this  function.                                                                                                 |
-| USAGE         | VARCHAR(7)  | Usage setting: GLYPH<SM590000> ALLOWED: The user profile is allowed to use the function. GLYPH<SM590000> DENIED: The user profile is not allowed to use the function. |
-| USER\_TYPE     | VARCHAR(5)  | Type of user profile: GLYPH<SM590000> USER: The user profile is a user. GLYPH<SM590000> GROUP: The user profile is a group.                                           |
+| USAGE         | VARCHAR(7)  | Usage setting: GLYPH&lt;SM590000&gt; ALLOWED: The user profile is allowed to use the function. GLYPH&lt;SM590000&gt; DENIED: The user profile is not allowed to use the function. |
+| USER\_TYPE     | VARCHAR(5)  | Type of user profile: GLYPH&lt;SM590000&gt; USER: The user profile is a user. GLYPH&lt;SM590000&gt; GROUP: The user profile is a group.                                           |
 
 To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.
 
@@ -273,11 +273,11 @@ Table 3-1 Special registers and their corresponding values
 
 Figure 3-5 shows the difference in the special register values when an adopted authority is used:
 
-- GLYPH<SM590000> A user connects to the server using the user profile ALICE.
-- GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE.
-- GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.
-- GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.
-- GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.
+- GLYPH&lt;SM590000&gt; A user connects to the server using the user profile ALICE.
+- GLYPH&lt;SM590000&gt; USER and CURRENT USER initially have the same value of ALICE.
+- GLYPH&lt;SM590000&gt; ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.
+- GLYPH&lt;SM590000&gt; While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.
+- GLYPH&lt;SM590000&gt; When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.
 
 Figure 3-5 Special registers and adopted authority
 
@@ -318,7 +318,7 @@ Here is an example of using the VERIFY\_GROUP\_FOR\_USER function:
 - 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:
 
 ```
-VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'MGR') VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JANE', 'MGR') VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JUDY', 'TONY')
+VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')
 ```
 
 RETURN
@@ -326,7 +326,7 @@ RETURN
 CASE
 
 ```
-WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE\_OF\_BIRTH WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'MGR' ) = 1 AND SESSION\_USER = EMPLOYEES . USER\_ID THEN EMPLOYEES . DATE\_OF\_BIRTH WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'MGR' ) = 1 AND SESSION\_USER <> EMPLOYEES . USER\_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE\_OF\_BIRTH ) || '-' || DAY (EMPLOYEES.DATE\_OF\_BIRTH )) ELSE NULL END ENABLE ;
+WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;
 ```
 
 - 2. The other column to mask in this example is the TAX\_ID information. In this example, the rules to enforce include the following ones:
@@ -339,7 +339,7 @@ WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES
 Example 3-9 Creating a mask on the TAX\_ID column
 
 ```
-CREATE MASK HR\_SCHEMA.MASK\_TAX\_ID\_ON\_EMPLOYEES ON HR\_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX\_ID RETURN CASE WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX\_ID WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'MGR' ) = 1 AND SESSION\_USER = EMPLOYEES . USER\_ID THEN EMPLOYEES . TAX\_ID WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'MGR' ) = 1 AND SESSION\_USER <> EMPLOYEES . USER\_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX\_ID , 8 , 4 ) ) WHEN VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX\_ID ELSE 'XXX-XX-XXXX' END ENABLE ;
+CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;
 ```
 
 - 3. Figure 3-10 shows the masks that are created in the HR\_SCHEMA.
@@ -386,7 +386,7 @@ Figure 4-69 Index advice with no RCAC
 <!-- image -->
 
 ```
-THEN C . CUSTOMER\_TAX\_ID WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER\_TAX\_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER\_TAX\_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK\_SCHEMA.MASK\_DRIVERS\_LICENSE\_ON\_CUSTOMERS ON BANK\_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER\_DRIVERS\_LICENSE\_NUMBER RETURN CASE WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER\_DRIVERS\_LICENSE\_NUMBER WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'TELLER' ) = 1 THEN C . CUSTOMER\_DRIVERS\_LICENSE\_NUMBER WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER\_DRIVERS\_LICENSE\_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK\_SCHEMA.MASK\_LOGIN\_ID\_ON\_CUSTOMERS ON BANK\_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER\_LOGIN\_ID RETURN CASE WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER\_LOGIN\_ID WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER\_LOGIN\_ID ELSE '*****' END ENABLE ; CREATE MASK BANK\_SCHEMA.MASK\_SECURITY\_QUESTION\_ON\_CUSTOMERS ON BANK\_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER\_SECURITY\_QUESTION RETURN CASE WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER\_SECURITY\_QUESTION WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER\_SECURITY\_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK\_SCHEMA.MASK\_SECURITY\_QUESTION\_ANSWER\_ON\_CUSTOMERS ON BANK\_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER\_SECURITY\_QUESTION\_ANSWER RETURN CASE WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER\_SECURITY\_QUESTION\_ANSWER WHEN QSYS2 . VERIFY\_GROUP\_FOR\_USER ( SESSION\_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER\_SECURITY\_QUESTION\_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK\_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;
+THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;
 ```
 
 Back cover
diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
index 790bda46..b6552e8c 100644
--- a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
+++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "ibm.com", "bbox": {"l": 36.900002, "t": 751.23, "r": 98.600998, "b": 765.105, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "/redbooks", "bbox": {"l": 98.580002, "t": 751.23, "r": 164.4585, "b": 765.105, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Redpaper", "bbox": {"l": 314.70001, "t": 711.50856, "r": 580.52002, "b": 773.772959, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Front cover", "bbox": {"l": 287.82001, "t": 28.54803000000004, "r": 418.83356, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Row and Column Access Control ", "bbox": {"l": 35.700001, "t": 84.58654999999987, "r": 584.64288, "b": 122.82097999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Support in IBM DB2 for i", "bbox": {"l": 35.700001, "t": 127.60668999999996, "r": 447.66919000000007, "b": 165.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Jim Bainbridge", "bbox": {"l": 509.22, "t": 575.71201, "r": 581.34119, "b": 586.392, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Hernando Bedoya", "bbox": {"l": 497.70001, "t": 589.752, "r": 581.3736, "b": 600.43199, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rob Bestgen", "bbox": {"l": 521.40002, "t": 603.73199, "r": 581.34003, "b": 614.4119900000001, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Mike Cain", "bbox": {"l": 534.12, "t": 617.71199, "r": 581.31598, "b": 628.39198, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Dan Cruikshank", "bbox": {"l": 505.43999999999994, "t": 631.75198, "r": 581.29077, "b": 642.4319800000001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Jim Denton", "bbox": {"l": 527.22003, "t": 645.73198, "r": 581.29926, "b": 656.41197, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Doug Mack", "bbox": {"l": 527.64001, "t": 659.71198, "r": 581.30884, "b": 670.39197, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Tom McKinley", "bbox": {"l": 514.38, "t": 673.75197, "r": 581.38678, "b": 684.43197, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Kent Milligan", "bbox": {"l": 518.82001, "t": 687.73197, "r": 581.33759, "b": 698.411972, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Implement roles and separation ", "bbox": {"l": 36.119999, "t": 296.13828, "r": 216.00064, "b": 308.58047, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of duties", "bbox": {"l": 36.119999, "t": 313.11838000000006, "r": 84.527145, "b": 325.56058, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Leverage", "bbox": {"l": 35.759315, "t": 350.13881999999995, "r": 80.409691, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "row permissions on ", "bbox": {"l": 151.8503, "t": 350.13881999999995, "r": 202.45404, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the", "bbox": {"l": 35.759315, "t": 367.11893, "r": 50.758106, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "database", "bbox": {"l": 86.755211, "t": 367.11893, "r": 107.75352000000001, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Protect", "bbox": {"l": 36.059887, "t": 404.13937, "r": 73.020607, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "columns by defining ", "bbox": {"l": 152.62831, "t": 404.13937, "r": 195.2753, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "column", "bbox": {"l": 36.059887, "t": 421.11948, "r": 73.311806, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "masks", "bbox": {"l": 73.311806, "t": 421.11948, "r": 117.33681, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_footer", "bbox": {"l": 36.900002, "t": 751.23, "r": 164.4585, "b": 765.105, "coord_origin": "TOPLEFT"}, "confidence": 0.718028724193573, "cells": [{"id": 0, "text": "ibm.com", "bbox": {"l": 36.900002, "t": 751.23, "r": 98.600998, "b": 765.105, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "/redbooks", "bbox": {"l": 98.580002, "t": 751.23, "r": 164.4585, "b": 765.105, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "picture", "bbox": {"l": 316.94049072265625, "t": 710.1278686523438, "r": 581.354736328125, "b": 774.4259033203125, "coord_origin": "TOPLEFT"}, "confidence": 0.5777533650398254, "cells": [{"id": 2, "text": "Redpaper", "bbox": {"l": 314.70001, "t": 711.50856, "r": 580.52002, "b": 773.772959, "coord_origin": "TOPLEFT"}}], "children": [{"id": 11, "label": "text", "bbox": {"l": 314.70001, "t": 711.50856, "r": 580.52002, "b": 773.772959, "coord_origin": "TOPLEFT"}, "confidence": 0.5870777368545532, "cells": [{"id": 2, "text": "Redpaper", "bbox": {"l": 314.70001, "t": 711.50856, "r": 580.52002, "b": 773.772959, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 12, "label": "text", "bbox": {"l": 287.82001, "t": 28.54803000000004, "r": 418.83356, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 3, "text": "Front cover", "bbox": {"l": 287.82001, "t": 28.54803000000004, "r": 418.83356, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 35.700001, "t": 84.58654999999987, "r": 584.64288, "b": 165.84113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8281131386756897, "cells": [{"id": 4, "text": "Row and Column Access Control ", "bbox": {"l": 35.700001, "t": 84.58654999999987, "r": 584.64288, "b": 122.82097999999996, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Support in IBM DB2 for i", "bbox": {"l": 35.700001, "t": 127.60668999999996, "r": 447.66919000000007, "b": 165.84113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "picture", "bbox": {"l": 33.090763092041016, "t": 293.03271484375, "r": 585.1502075195312, "b": 702.4530639648438, "coord_origin": "TOPLEFT"}, "confidence": 0.5889522433280945, "cells": [{"id": 6, "text": "Jim Bainbridge", "bbox": {"l": 509.22, "t": 575.71201, "r": 581.34119, "b": 586.392, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Hernando Bedoya", "bbox": {"l": 497.70001, "t": 589.752, "r": 581.3736, "b": 600.43199, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rob Bestgen", "bbox": {"l": 521.40002, "t": 603.73199, "r": 581.34003, "b": 614.4119900000001, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Mike Cain", "bbox": {"l": 534.12, "t": 617.71199, "r": 581.31598, "b": 628.39198, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Dan Cruikshank", "bbox": {"l": 505.43999999999994, "t": 631.75198, "r": 581.29077, "b": 642.4319800000001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Jim Denton", "bbox": {"l": 527.22003, "t": 645.73198, "r": 581.29926, "b": 656.41197, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Doug Mack", "bbox": {"l": 527.64001, "t": 659.71198, "r": 581.30884, "b": 670.39197, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Tom McKinley", "bbox": {"l": 514.38, "t": 673.75197, "r": 581.38678, "b": 684.43197, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Kent Milligan", "bbox": {"l": 518.82001, "t": 687.73197, "r": 581.33759, "b": 698.411972, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "Implement roles and separation ", "bbox": {"l": 36.119999, "t": 296.13828, "r": 216.00064, "b": 308.58047, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of duties", "bbox": {"l": 36.119999, "t": 313.11838000000006, "r": 84.527145, "b": 325.56058, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Leverage", "bbox": {"l": 35.759315, "t": 350.13881999999995, "r": 80.409691, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "row permissions on ", "bbox": {"l": 151.8503, "t": 350.13881999999995, "r": 202.45404, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the", "bbox": {"l": 35.759315, "t": 367.11893, "r": 50.758106, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "database", "bbox": {"l": 86.755211, "t": 367.11893, "r": 107.75352000000001, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Protect", "bbox": {"l": 36.059887, "t": 404.13937, "r": 73.020607, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "columns by defining ", "bbox": {"l": 152.62831, "t": 404.13937, "r": 195.2753, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "column", "bbox": {"l": 36.059887, "t": 421.11948, "r": 73.311806, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "masks", "bbox": {"l": 73.311806, "t": 421.11948, "r": 117.33681, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}], "children": [{"id": 5, "label": "text", "bbox": {"l": 497.70001, "t": 575.71201, "r": 581.38678, "b": 698.411972, "coord_origin": "TOPLEFT"}, "confidence": 0.7621798515319824, "cells": [{"id": 6, "text": "Jim Bainbridge", "bbox": {"l": 509.22, "t": 575.71201, "r": 581.34119, "b": 586.392, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Hernando Bedoya", "bbox": {"l": 497.70001, "t": 589.752, "r": 581.3736, "b": 600.43199, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "Rob Bestgen", "bbox": {"l": 521.40002, "t": 603.73199, "r": 581.34003, "b": 614.4119900000001, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "Mike Cain", "bbox": {"l": 534.12, "t": 617.71199, "r": 581.31598, "b": 628.39198, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Dan Cruikshank", "bbox": {"l": 505.43999999999994, "t": 631.75198, "r": 581.29077, "b": 642.4319800000001, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Jim Denton", "bbox": {"l": 527.22003, "t": 645.73198, "r": 581.29926, "b": 656.41197, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Doug Mack", "bbox": {"l": 527.64001, "t": 659.71198, "r": 581.30884, "b": 670.39197, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Tom McKinley", "bbox": {"l": 514.38, "t": 673.75197, "r": 581.38678, "b": 684.43197, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Kent Milligan", "bbox": {"l": 518.82001, "t": 687.73197, "r": 581.33759, "b": 698.411972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 36.119999, "t": 296.13828, "r": 216.00064, "b": 325.56058, "coord_origin": "TOPLEFT"}, "confidence": 0.8502885103225708, "cells": [{"id": 15, "text": "Implement roles and separation ", "bbox": {"l": 36.119999, "t": 296.13828, "r": 216.00064, "b": 308.58047, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "of duties", "bbox": {"l": 36.119999, "t": 313.11838000000006, "r": 84.527145, "b": 325.56058, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 35.759315, "t": 350.13881999999995, "r": 202.45404, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}, "confidence": 0.8639549016952515, "cells": [{"id": 17, "text": "Leverage", "bbox": {"l": 35.759315, "t": 350.13881999999995, "r": 80.409691, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "row permissions on ", "bbox": {"l": 151.8503, "t": 350.13881999999995, "r": 202.45404, "b": 362.58102, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "the", "bbox": {"l": 35.759315, "t": 367.11893, "r": 50.758106, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "database", "bbox": {"l": 86.755211, "t": 367.11893, "r": 107.75352000000001, "b": 379.56112999999993, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 36.059887, "t": 404.13937, "r": 195.2753, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8421130180358887, "cells": [{"id": 21, "text": "Protect", "bbox": {"l": 36.059887, "t": 404.13937, "r": 73.020607, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "columns by defining ", "bbox": {"l": 152.62831, "t": 404.13937, "r": 195.2753, "b": 416.58157, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "column", "bbox": {"l": 36.059887, "t": 421.11948, "r": 73.311806, "b": 433.56167999999997, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "masks", "bbox": {"l": 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473134279251099, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.892595112323761, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "29", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "32", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9508247971534729, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1510467529297, "t": 132.0303497314453, "r": 547.5270385742188, "b": 715.6527099609375, "coord_origin": "TOPLEFT"}, "confidence": 0.9803491234779358, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. 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This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.987220287322998, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. 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A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.982377827167511, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.5288543701172, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41448974609375, "t": 527.24462890625, "r": 252.0884246826172, "b": 635.3829956054688, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174706935882568, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.987220287322998, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.982377827167511, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.5288543701172, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.41448974609375, "t": 527.24462890625, "r": 252.0884246826172, "b": 635.3829956054688, "coord_origin": "TOPLEFT"}, "confidence": 0.987165629863739, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9040938019752502, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865864515304565, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734501242637634, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8513590097427368, "cells": [{"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651356935501099, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663436412811279, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077331781387329, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967196106910706, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621996879577637, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650284051895142, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848749041557312, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870818853378296, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889445662498474, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476425051689148, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578531742095947, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578531742095947, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92469787597656, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": 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612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166074395179749, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.969738245010376, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551493525505066, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808837413787842, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 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"cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": 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"TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631589293479919, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": 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0.5340578556060791, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171271562576294, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373097658157349, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167067050933838, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656645059585571, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.985310435295105, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52508544921875, "t": 289.7249755859375, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 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0.9551493525505066, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410473704338074, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9417493939399719, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562166929244995, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682288765907288, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.9056946039199829, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636374711990356, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737266302108765, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9197835326194763, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.9647642970085144, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8165056705474854, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808837413787842, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631589293479919, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}, "confidence": 0.5345606803894043, "cells": [{"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5818131566047668, "cells": [{"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5035313963890076, "cells": [{"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5340578556060791, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171271562576294, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373097658157349, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167067050933838, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656645059585571, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.985310435295105, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52508544921875, "t": 289.7249755859375, "r": 545.87060546875, "b": 442.0505065917969, "coord_origin": "TOPLEFT"}, "confidence": 0.9851696491241455, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 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"USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 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GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166074395179749, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9529877305030823, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987305164337158, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789127707481384, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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Roles and separation of duties"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987305164337158, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789127707481384, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665537476539612, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729604125022888, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672254323959351, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557499885559082, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130505323410034, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875164151191711, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.946666419506073, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 16, "label": "picture", "bbox": {"l": 135.64842224121094, "t": 384.17364501953125, "r": 301.23675537109375, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221464514732361, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": 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"used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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"TOPLEFT"}, "confidence": 0.7875164151191711, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253707528114319, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.946666419506073, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.950442373752594, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.9811323285102844, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.96592116355896, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696776866912842, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.9783982038497925, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.54624938964844, "t": 103.41888427734375, "r": 542.0460815429688, "b": 204.27162170410156, "coord_origin": "TOPLEFT"}, "confidence": 0.9731297492980957, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64842224121094, "t": 384.17364501953125, 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"confidence": 0.9557499885559082, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130505323410034, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": 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342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370970487594604, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 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281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370970487594604, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9132352471351624, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237532615661621, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338627457618713, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.951445996761322, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.9512304067611694, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066406011581421, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55625915527344, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868636131286621, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187918305397034, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505078196525574, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606334805488586, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.5897265076637268, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.7785210013389587, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.867893397808075, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467737078666687, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705168604850769, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469640254974365, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003504037857056, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488206267356873, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411226868629456, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.955692708492279, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751050233840942, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.9444271922111511, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768576622009277, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059444665908813, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6570088863372803, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002442836761475, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.5985879898071289, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431817173957825, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449260234832764, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499982595443726, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.80186462402344, "t": 95.38243103027344, "r": 547.1146850585938, "b": 170.03213500976562, "coord_origin": "TOPLEFT"}, "confidence": 0.9635391235351562, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.98520278930664, "t": 427.9049072265625, "r": 530.0477905273438, "b": 646.1395874023438, "coord_origin": "TOPLEFT"}, "confidence": 0.9801457524299622, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161999821662903, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056529998779297, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.763218104839325, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8993997573852539, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9430769085884094, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.49323785305023193, "t": 0.0, "r": 610.2305297851562, "b": 791.6546630859375, "coord_origin": "TOPLEFT"}, "confidence": 0.6497198343276978, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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668.62009, "r": 530.54413, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 117, "text": "26", "bbox": {"l": 536.08301, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}}, {"id": 118, "text": "3.6.6", "bbox": {"l": 151.19717, "t": 681.15973, "r": 173.38359, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 119, "text": "Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.93019, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 120, "text": "28", "bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}}, {"id": 121, "text": "3.6.7", "bbox": {"l": 151.19717, "t": 693.63961, "r": 173.32332, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 122, "text": "Demonstrating data access with RCAC", "bbox": {"l": 178.85486, "t": 693.63961, "r": 350.80011, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 123, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 356.33163, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 124, "text": "29", "bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}}, {"id": 125, "text": "3.6.8", "bbox": {"l": 151.19717, "t": 706.119492, "r": 173.44592, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 126, "text": "Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00812, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}}, {"id": 127, "text": "32", "bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8926277160644531, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509142637252808, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "document_index", "bbox": {"l": 136.1496124267578, "t": 132.0330352783203, "r": 547.5267944335938, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803681373596191, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, 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"TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 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false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9473597407341003, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8926277160644531, "cells": [{"id": 1, "text": "iii", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25928, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "iii"}, {"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509142637252808, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1496124267578, "t": 132.0330352783203, "r": 547.5267944335938, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803681373596191, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 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. . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too!", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "18", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 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"end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "21", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79701, "t": 593.62131, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "22", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "23", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0882, "t": 643.66034, "r": 547.15076, "b": 652.87334, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "24", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 656.14021, "r": 530.53412, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.073, "t": 656.14021, "r": 547.15088, "b": 665.35321, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "25", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 668.62009, "r": 339.45105, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 344.98996, "t": 668.62009, "r": 547.16089, "b": 677.83309, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 681.15973, "r": 530.54102, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.08765, "t": 681.15973, "r": 547.18085, "b": 690.37273, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "28", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 693.63961, "r": 530.57507, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10663, "t": 693.63961, "r": 547.16968, "b": 702.852615, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 41, "end_row_offset_idx": 42, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "29", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19717, "t": 706.119492, "r": 530.43628, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99847, "t": 706.119492, "r": 547.12286, "b": 715.332497, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 42, "end_row_offset_idx": 43, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "32", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "section_header", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9509142637252808, "cells": [{"id": 2, "text": "Contents", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 168.73441, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Contents"}, {"label": "document_index", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "document_index", "bbox": {"l": 136.1496124267578, "t": 132.0330352783203, "r": 547.5267944335938, "b": 715.6515502929688, "coord_origin": "TOPLEFT"}, "confidence": 0.9803681373596191, "cells": [{"id": 3, "text": "Notices", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 172.89404, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "bbox": {"l": 175.01952, "t": 132.64862000000005, "r": 547.18982, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Trademarks", "bbox": {"l": 136.79901, "t": 145.12847999999997, "r": 189.86537, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 195.39685, "t": 145.12847999999997, "r": 530.05121, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "viii", "bbox": {"l": 535.5827, "t": 145.12847999999997, "r": 547.18286, "b": 154.34149000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "DB2 for i Center of Excellence", "bbox": {"l": 136.79901, "t": 167.62811, "r": 279.39731, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "bbox": {"l": 280.61942, "t": 167.62811, "r": 547.1908, "b": 176.84113000000002, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Preface", "bbox": {"l": 136.79901, "t": 190.12775, "r": 172.84424, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 175.01852, "t": 190.12775, "r": 547.18286, "b": 199.34076000000005, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "bbox": {"l": 136.79803, "t": 202.60760000000005, "r": 547.18085, "b": 211.82061999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Now you can become a published author, too!", "bbox": {"l": 136.79803, "t": 215.14721999999995, "r": 339.18292, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 344.71411, "t": 215.14721999999995, "r": 530.00812, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "xiii", "bbox": {"l": 535.53925, "t": 215.14721999999995, "r": 547.13879, "b": 224.36023, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 136.79803, "t": 227.62707999999998, "r": 529.99506, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "xiii", "bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Stay connected to IBM Redbooks", "bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 529.48242, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "xiv", "bbox": {"l": 534.99829, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Chapter 1. Securing and protecting IBM DB2 data", "bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 373.17566, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 375.11798, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "1", "bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "1.1", "bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 150.88702, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "bbox": {"l": 156.5226, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "1.2", "bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 150.62746, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15923, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "2", "bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "1.3", "bbox": {"l": 136.79807, "t": 299.62595, "r": 150.84943, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "bbox": {"l": 156.46996, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "1.3.1", "bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 173.38289, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.92932, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "4", "bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "1.3.2", "bbox": {"l": 151.1972, "t": 324.64548, "r": 173.4189, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97432, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "5", "bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "Chapter 2. Roles and separation of duties", "bbox": {"l": 136.79704, "t": 347.14511, "r": 336.82071, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 338.99701, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "7", "bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "2.1", "bbox": {"l": 136.79704, "t": 359.14493, "r": 150.644, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18277, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "8", "bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "2.1.1", "bbox": {"l": 151.1972, "t": 371.62482, "r": 173.60995, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "bbox": {"l": 176.41154, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "8", "bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "2.1.2", "bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 173.41664, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97151, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "8", "bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "2.1.3", "bbox": {"l": 151.1972, "t": 396.64435, "r": 173.41859, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.97394, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "9", "bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "2.1.4", "bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 173.38629, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "Database Information function: QIBM_DB_SYSMON", "bbox": {"l": 178.93356, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": ". . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 536.08411, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "9", "bbox": {"l": 541.63135, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "2.1.5", "bbox": {"l": 151.1972, "t": 421.60413, "r": 173.44926, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.01228, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "9", "bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "2.1.6", "bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 173.32208, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8533, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "10", "bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "2.1.7", "bbox": {"l": 151.1972, "t": 446.62366, "r": 173.35822, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "bbox": {"l": 178.89848, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "10", "bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "2.2", "bbox": {"l": 136.79704, "t": 459.10355, "r": 150.85457, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "bbox": {"l": 156.47758, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Chapter 3. Row and Column Access Control", "bbox": {"l": 136.79703, "t": 481.60318, "r": 348.68503, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 350.09741, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "13", "bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "3.1", "bbox": {"l": 136.79703, "t": 493.603, "r": 150.70105, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.26266, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 72, "text": "14", "bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}}, {"id": 73, "text": "3.1.1", "bbox": {"l": 151.19719, "t": 506.14264, "r": 173.35429, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 74, "text": "Row permission and column mask definitions", "bbox": {"l": 178.89357, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 75, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 383.74713, "t": 506.14264, "r": 530.5379, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 76, "text": "14", "bbox": {"l": 536.07721, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}}, {"id": 77, "text": "3.1.2", "bbox": {"l": 151.19719, "t": 518.62253, "r": 173.44292, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 78, "text": "Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 179.00435, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 79, "text": "16", "bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}}, {"id": 80, "text": "3.2", "bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 150.64432, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 81, "text": "Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.18323, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 82, "text": "18", "bbox": {"l": 536.06702, "t": 531.1621700000001, "r": 547.14484, "b": 540.37517, "coord_origin": "TOPLEFT"}}, {"id": 83, "text": "3.2.1", "bbox": {"l": 151.19719, "t": 543.64204, "r": 173.41321, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 84, "text": "Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.96722, "t": 543.64204, "r": 530.49786, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 85, "text": "18", "bbox": {"l": 536.05188, "t": 543.64204, "r": 547.15991, "b": 552.8550399999999, "coord_origin": "TOPLEFT"}}, {"id": 86, "text": "3.2.2", "bbox": {"l": 151.19719, "t": 556.12192, "r": 173.35269, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 87, "text": "Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.89156, "t": 556.12192, "r": 530.56024, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 88, "text": "19", "bbox": {"l": 536.09912, "t": 556.12192, "r": 547.17688, "b": 565.33492, "coord_origin": "TOPLEFT"}}, {"id": 89, "text": "3.3", "bbox": {"l": 136.79703, "t": 568.66156, "r": 150.62514, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 90, "text": "VERIFY_GROUP_FOR_USER function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.15639, "t": 568.66156, "r": 530.53027, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 91, "text": "20", "bbox": {"l": 536.06152, "t": 568.66156, "r": 547.12402, "b": 577.87456, "coord_origin": "TOPLEFT"}}, {"id": 92, "text": "3.4", "bbox": {"l": 136.79703, "t": 581.14143, "r": 150.63004, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 93, "text": "Establishing and controlling accessibility by using the RCAC rule text . . . . . . . . . . . . .", "bbox": {"l": 156.16325, "t": 581.14143, "r": 530.62994, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 94, "text": "21", "bbox": {"l": 536.16315, "t": 581.14143, "r": 547.22955, "b": 590.35443, "coord_origin": "TOPLEFT"}}, {"id": 95, "text": "3.5", "bbox": {"l": 136.79701, "t": 593.62131, "r": 150.64413, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 96, "text": "SELECT, INSERT, and UPDATE behavior with RCAC", "bbox": {"l": 156.18298, "t": 593.62131, "r": 394.7818, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 97, "text": ". . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 400.32065, "t": 593.62131, "r": 530.48358, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 98, "text": "22", "bbox": {"l": 536.0224, "t": 593.62131, "r": 547.1001, "b": 602.8343, "coord_origin": "TOPLEFT"}}, {"id": 99, "text": "3.6", "bbox": {"l": 136.79701, "t": 606.16095, "r": 150.6642, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 100, "text": "Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 156.21107, "t": 606.16095, "r": 530.56512, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 101, "text": "22", "bbox": {"l": 536.112, "t": 606.16095, "r": 547.20575, "b": 615.37395, "coord_origin": "TOPLEFT"}}, {"id": 102, "text": "3.6.1", "bbox": {"l": 151.19717, "t": 618.64082, "r": 173.41692, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 103, "text": "Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . .", "bbox": {"l": 178.97185, "t": 618.64082, "r": 530.49139, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 104, "text": "23", "bbox": {"l": 536.04633, "t": 618.64082, "r": 547.15619, "b": 627.85382, "coord_origin": "TOPLEFT"}}, {"id": 105, "text": "3.6.2", "bbox": {"l": 151.19717, "t": 631.1206999999999, "r": 173.32271, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 106, "text": "Creating group profiles for the users and their roles . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.8541, "t": 631.1206999999999, "r": 530.56458, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 107, "text": "23", "bbox": {"l": 536.09601, "t": 631.1206999999999, "r": 547.15875, "b": 640.3336899999999, "coord_origin": "TOPLEFT"}}, {"id": 108, "text": "3.6.3", "bbox": {"l": 151.19717, "t": 643.66034, "r": 173.32227, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 109, "text": "Demonstrating data access without RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "bbox": {"l": 178.85353, "t": 643.66034, "r": 530.55695, "b": 652.87334, "coord_origin": "TOPLEFT"}}, {"id": 110, "text": "24", 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"b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.5495, "t": 227.62707999999998, "r": 547.19788, "b": 236.84009000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "xiii", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 240.10693000000003, "r": 284.02866, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.54449, "t": 240.10693000000003, "r": 547.12115, "b": 249.31994999999995, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 262.60657000000003, "r": 536.09589, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64685, "t": 262.60657000000003, "r": 547.19781, "b": 271.81958, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "1", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79808, "t": 274.60637999999994, "r": 549.84723, "b": 283.8194, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 287.14606000000003, "r": 536.12933, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66113, "t": 287.14606000000003, "r": 547.19287, "b": 296.35904, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79807, "t": 299.62595, "r": 549.84723, "b": 308.83893, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 312.1058300000001, "r": 536.05511, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6015, "t": 312.1058300000001, "r": 547.14795, "b": 321.3188200000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 324.64548, "r": 536.08008, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.6355, "t": 324.64548, "r": 547.19092, "b": 333.8584599999999, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "5", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 347.14511, "r": 536.09088, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.64282, "t": 347.14511, "r": 547.19476, "b": 356.35809, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "7", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 359.14493, "r": 536.12714, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.66589, "t": 359.14493, "r": 547.20471, "b": 368.35791, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 371.62482, "r": 535.9527, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.55585, "t": 371.62482, "r": 547.15906, "b": 380.8378000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 384.10470999999995, "r": 536.04108, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59595, "t": 384.10470999999995, "r": 547.15082, "b": 393.31769, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "8", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 396.64435, "r": 536.07489, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.63025, "t": 396.64435, "r": 547.18561, "b": 405.85733, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 409.12424000000004, "r": 411.27048, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 416.81775, "t": 409.12424000000004, "r": 547.17865, "b": 418.33722, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 421.60413, "r": 536.03589, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 541.59894, "t": 421.60413, "r": 547.16193, "b": 430.81711, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "9", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 434.1437700000001, "r": 530.57318, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.10443, "t": 434.1437700000001, "r": 547.16687, "b": 443.35675, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.1972, "t": 446.62366, "r": 530.53522, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.0755, "t": 446.62366, "r": 547.15601, "b": 455.83663999999993, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79704, "t": 459.10355, "r": 547.25659, "b": 468.31653, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 481.60318, "r": 530.53961, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.09167, "t": 481.60318, "r": 547.1958, "b": 490.81616, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "13", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 493.603, "r": 530.4809, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 536.04248, "t": 493.603, "r": 547.16571, "b": 502.81598, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 506.14264, "r": 378.20786, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 383.74713, "t": 506.14264, "r": 547.15576, "b": 515.35562, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 151.19719, "t": 518.62253, "r": 530.43475, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 535.99622, "t": 518.62253, "r": 547.11908, "b": 527.83551, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "16", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 136.79703, "t": 531.1621700000001, "r": 530.52808, "b": 540.37517, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). 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A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. 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A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"id": 2, "text": "Preface", "bbox": {"l": 64.800003, "t": 73.84802000000002, "r": 151.46161, "b": 96.04803000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79984, "t": 132.64862000000005, "r": 547.30823, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"id": 3, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "bbox": {"l": 136.8, "t": 132.64862000000005, "r": 542.91888, "b": 141.86163, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "bbox": {"l": 136.79984, "t": 144.64844000000005, "r": 526.65509, "b": 153.86145, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "bbox": {"l": 136.79984, "t": 156.64824999999996, "r": 536.82135, "b": 165.86127, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "bbox": {"l": 136.79987, "t": 168.64806999999996, "r": 544.67975, "b": 177.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "bbox": {"l": 136.79987, "t": 180.64788999999996, "r": 547.30823, "b": 189.86090000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "database environment.", "bbox": {"l": 136.79987, "t": 192.64770999999996, "r": 238.32117, "b": 201.86072000000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79987, "t": 214.60748, "r": 546.4657, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"id": 9, "text": "This paper is intended for database engineers, data-centric application developers, and ", "bbox": {"l": 136.79987, "t": 214.60748, "r": 524.18518, "b": 223.82050000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "bbox": {"l": 136.79987, "t": 226.6073, "r": 546.4657, "b": 235.82030999999995, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "bbox": {"l": 136.79987, "t": 238.60712, "r": 521.25488, "b": 247.82012999999995, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "database concepts, and SQL is assumed.", "bbox": {"l": 136.79987, "t": 250.60693000000003, "r": 321.69434, "b": 259.81994999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"id": 13, "text": "Authors", "bbox": {"l": 64.800003, "t": 288.3006, "r": 125.36661, "b": 303.0636, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 341.84152, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"id": 14, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "bbox": {"l": 136.8, "t": 320.62871999999993, "r": 547.23669, "b": 329.8417099999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "bbox": {"l": 136.8, "t": 332.62854, "r": 505.05518, "b": 341.84152, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.39957, "t": 375.64877, "r": 541.25079, "b": 516.85974, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"id": 16, "text": "Jim Bainbridge", "bbox": {"l": 263.39957, "t": 375.64877, "r": 335.7251, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " is a senior DB2 consultant on the DB2 for i ", "bbox": {"l": 335.69922, "t": 375.64877, "r": 529.34259, "b": 384.86176, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "Center of Excellence team in the IBM Lab Services and ", "bbox": {"l": 263.3996, "t": 387.64859, "r": 511.50717, "b": 396.86157, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "Training organization. His primary role is training and ", "bbox": {"l": 263.3996, "t": 399.64841, "r": 499.077, "b": 408.86139, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "implementation services for IBM DB2 Web Query for i and ", "bbox": {"l": 263.3996, "t": 411.64822, "r": 522.51996, "b": 420.86121, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "business analytics. Jim began his career with IBM 30 years ago ", "bbox": {"l": 263.3996, "t": 423.64804, "r": 541.25079, "b": 432.86102, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "in the IBM Rochester Development Lab, where he developed ", "bbox": {"l": 263.3996, "t": 435.64786, "r": 534.71411, "b": 444.86084, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "cooperative processing products that paired IBM PCs with IBM ", "bbox": {"l": 263.3996, "t": 447.64767, "r": 541.22375, "b": 456.86066, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "bbox": {"l": 263.3996, "t": 459.64749, "r": 528.91016, "b": 468.86047, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "numerous technical roles, including independent software ", "bbox": {"l": 263.3996, "t": 471.64731, "r": 520.24207, "b": 480.86029, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "vendors technical support on a broad range of IBM ", "bbox": {"l": 263.3996, "t": 483.64713, "r": 490.6967200000001, "b": 492.86011, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "technologies and products, and supporting customers in the ", "bbox": {"l": 263.3996, "t": 495.64694, "r": 530.95514, "b": 504.85992, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "IBM Executive Briefing Center and IBM Project Office.", "bbox": {"l": 263.3996, "t": 507.64676, "r": 501.62973, "b": 516.85974, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.3996, "t": 527.62653, "r": 541.27374, "b": 680.83716, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"id": 29, "text": "Hernando Bedoya", "bbox": {"l": 263.3996, "t": 527.62653, "r": 348.38229, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": " is a Senior IT Specialist at STG Lab ", "bbox": {"l": 348.41916, "t": 527.62653, "r": 512.3429, "b": 536.83952, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Services and Training in Rochester, Minnesota. He writes ", "bbox": {"l": 263.3996, "t": 539.62633, "r": 519.26306, "b": 548.83932, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "extensively and teaches IBM classes worldwide in all areas of ", "bbox": {"l": 263.3996, "t": 551.62613, "r": 538.40308, "b": 560.8391300000001, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "bbox": {"l": 263.3996, "t": 563.62593, "r": 533.95715, "b": 572.83893, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "bbox": {"l": 263.3996, "t": 575.62573, "r": 496.94464, "b": 584.8387299999999, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "publications. He also worked for IBM Colombia as an IBM ", "bbox": {"l": 263.3996, "t": 587.62553, "r": 520.38562, "b": 596.83853, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "bbox": {"l": 263.3996, "t": 599.62534, "r": 535.99078, "b": 608.83833, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "countries. He has 28 years of experience in the computing field ", "bbox": {"l": 263.3996, "t": 611.62514, "r": 541.27374, "b": 620.83813, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "and has taught database classes in Colombian universities. He ", "bbox": {"l": 263.3996, "t": 623.62494, "r": 541.26465, "b": 632.83794, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "holds a Master\u2019s degree in Computer Science from EAFIT, ", "bbox": {"l": 263.3996, "t": 635.62474, "r": 523.22211, "b": 644.8377399999999, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Colombia. His areas of expertise are database technology, ", "bbox": {"l": 263.3996, "t": 647.62454, "r": 524.77386, "b": 656.83754, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "performance, and data warehousing. Hernando can be ", "bbox": {"l": 263.3996, "t": 659.62434, "r": 508.27124, "b": 668.83735, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "contacted at ", "bbox": {"l": 263.3996, "t": 671.62415, "r": 320.63568, "b": 680.83716, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "hbedoya@us.ibm.com", "bbox": {"l": 320.63971, "t": 671.77356, "r": 410.57852, "b": 680.54832, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": ".", "bbox": {"l": 410.5795, "t": 671.62415, "r": 413.34839, "b": 680.83716, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master\u2019s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"id": 1, "text": "xi", "bbox": {"l": 538.85999, "t": 754.848721, "r": 547.25031, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Chapter 1.", "bbox": {"l": 81.0, "t": 268.54272000000003, "r": 115.13253, "b": 274.98071000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Securing and protecting IBM DB2 ", "bbox": {"l": 136.8, "t": 254.88635, "r": 547.30475, "b": 278.91785000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "data", "bbox": {"l": 136.8, "t": 285.84671, "r": 190.29802, "b": 309.8782, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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with reports of data breaches and cyber-attacks impacting ", "bbox": {"l": 136.8, "t": 348.70871, "r": 542.25665, "b": 357.92169, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 ", "bbox": {"l": 136.80096, "t": 360.70853, "r": 544.96643, "b": 369.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "bbox": {"l": 136.79965, "t": 372.70853, "r": 529.53839, "b": 381.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ ", "bbox": {"l": 136.79965, "t": 384.7083400000001, "r": 535.32874, "b": 393.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "bbox": {"l": 136.80026, "t": 396.70853, "r": 521.64374, "b": 405.92150999999996, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "bbox": {"l": 136.80026, "t": 408.7083400000001, "r": 547.13135, "b": 417.92133000000007, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "bbox": {"l": 136.80023, "t": 420.70816, "r": 547.25403, "b": 429.92114, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center$^{1}$ reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute$^{2}$ revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 527.2063, "b": 487.94016, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"id": 12, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "bbox": {"l": 136.80023, "t": 442.7277199999999, "r": 525.06482, "b": 451.9407, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "bbox": {"l": 136.80025, "t": 454.72754000000003, "r": 518.26825, "b": 463.94052, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "bbox": {"l": 136.80025, "t": 466.72736, "r": 527.2063, "b": 475.94034, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "longer an option; it is a requirement.", "bbox": {"l": 136.80025, "t": 478.72717, "r": 296.31067, "b": 487.94016, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 521.89978, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"id": 16, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "bbox": {"l": 136.80025, "t": 500.68698, "r": 547.15515, "b": 509.89996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "are covered in this chapter:", "bbox": {"l": 136.80025, "t": 512.6868, "r": 257.28036, "b": 521.89978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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All rights reserved.", "bbox": {"l": 64.800003, "t": 755.538002, "r": 257.24335, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"id": 1, "text": "1", "bbox": {"l": 541.67987, "t": 754.848721, "r": 547.21765, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"id": 2, "text": "1.1", "bbox": {"l": 64.800003, "t": 74.34069999999997, "r": 87.524292, "b": 89.1037, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Security fundamentals", "bbox": {"l": 92.069145, "t": 74.34069999999997, "r": 267.40582, "b": 89.1037, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"id": 4, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "bbox": {"l": 136.8, "t": 106.6087, "r": 545.00482, "b": 115.82172000000003, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "information assets that must be described:", "bbox": {"l": 136.8, "t": 118.60852, "r": 324.47229, "b": 127.82153000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.12487999999996, "r": 547.16425, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 135.79749000000004, "r": 141.78, "b": 144.57227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "First, and most important, is the definition of a company\u2019s ", "bbox": {"l": 151.20016, "t": 135.64806999999996, "r": 406.67715, "b": 144.86108000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "security policy", "bbox": {"l": 406.67999, "t": 135.12487999999996, "r": 471.03815, "b": 145.18262000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": ". Without a ", "bbox": {"l": 470.04001000000005, "t": 135.64862000000005, "r": 520.59796, "b": 144.86163, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "bbox": {"l": 151.19949, "t": 147.64844000000005, "r": 547.16425, "b": 156.86145, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "and storing information by who, what, when, where, and how. A security policy should ", "bbox": {"l": 151.19948, "t": 159.64824999999996, "r": 531.02008, "b": 168.86127, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "minimally address three things: confidentiality, integrity, and availability.", "bbox": {"l": 151.19948, "t": 171.64806999999996, "r": 463.3578499999999, "b": 180.86108000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> First, and most important, is the definition of a company\u2019s security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.19946, "t": 188.62787000000003, "r": 547.26086, "b": 269.83978, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"id": 13, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "bbox": {"l": 151.19948, "t": 188.62787000000003, "r": 541.70514, "b": 197.84087999999997, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "bbox": {"l": 151.19948, "t": 200.62769000000003, "r": 534.83002, "b": 209.84069999999997, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "security assessments for companies without regard to the security policy. Although these ", "bbox": {"l": 151.19948, "t": 212.62750000000005, "r": 545.79773, "b": 221.84051999999997, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "assessments can be useful for observing how the system is defined and how data is being ", "bbox": {"l": 151.19948, "t": 224.62732000000005, "r": 547.26086, "b": 233.84033, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "bbox": {"l": 151.19948, "t": 236.62714000000005, "r": 543.91528, "b": 245.84015, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "bbox": {"l": 151.19948, "t": 248.62694999999997, "r": 547.25989, "b": 257.83997, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "changes in the security settings that are captured.", "bbox": {"l": 151.19946, "t": 260.62676999999996, "r": 371.8692, "b": 269.83978, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"id": 20, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "bbox": {"l": 151.19946, "t": 277.60657000000003, "r": 541.992, "b": 286.81958, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.7993, "t": 294.12496999999996, "r": 547.15826, "b": 375.8606, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"id": 21, "text": "GLYPH<SM590000>", "bbox": {"l": 136.7993, "t": 294.7955600000001, "r": 141.7793, "b": 303.57034, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "The second fundamental in securing data assets is the use of ", "bbox": {"l": 151.19946, "t": 294.64618, "r": 425.86029, "b": 303.85916, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "resource security", "bbox": {"l": 425.82001, "t": 294.12496999999996, "r": 501.60065, "b": 304.18265, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": ". If ", "bbox": {"l": 500.64001, "t": 294.64871, "r": 514.49933, "b": 303.86169, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "implemented properly, resource security prevents data breaches from both internal and ", "bbox": {"l": 151.20038, "t": 306.64853, "r": 537.87421, "b": 315.86151, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "bbox": {"l": 151.20038, "t": 318.64834999999994, "r": 541.33636, "b": 327.86133, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "policy that defines who should have access to what information resources. A hacker might ", "bbox": {"l": 151.20038, "t": 330.64816, "r": 547.15826, "b": 339.86115, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "be good enough to get through your company firewalls and sift his way through to your ", "bbox": {"l": 151.20038, "t": 342.64798, "r": 534.86066, "b": 351.86096, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "bbox": {"l": 151.20038, "t": 354.6478, "r": 517.00739, "b": 363.86078, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "compromise your information assets.", "bbox": {"l": 151.20038, "t": 366.64761, "r": 314.03534, "b": 375.8606, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 409.82022, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"id": 31, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "bbox": {"l": 136.80022, "t": 388.60742, "r": 535.36169, "b": 397.82040000000006, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "bbox": {"l": 136.80022, "t": 400.60724, "r": 532.755, "b": 409.82022, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"id": 33, "text": "1.2", "bbox": {"l": 64.800003, "t": 438.30072, "r": 87.415726, "b": 453.06372, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Current state of IBM i security", "bbox": {"l": 91.93885, "t": 438.30072, "r": 323.38391, "b": 453.06372, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.62872, "r": 547.31824, "b": 515.84116, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"id": 35, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "bbox": {"l": 136.8, "t": 470.62872, "r": 530.30463, "b": 479.84171, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "bbox": {"l": 136.8, "t": 482.62854, "r": 547.31824, "b": 491.84152, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "bbox": {"l": 136.8, "t": 494.62836, "r": 547.19586, "b": 503.84134, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "system value is *CHANGE.", "bbox": {"l": 136.8, "t": 506.62817, "r": 257.04709, "b": 515.84116, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.64774, "r": 547.28442, "b": 585.85994, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"id": 39, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "bbox": {"l": 136.8, "t": 528.64774, "r": 513.90448, "b": 537.86073, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "bbox": {"l": 136.8, "t": 540.6475399999999, "r": 547.28442, "b": 549.86053, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "bbox": {"l": 136.8, "t": 552.64734, "r": 546.27533, "b": 561.86034, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "A tighter implementation is required if you really want to protect one of your company\u2019s most ", "bbox": {"l": 136.8, "t": 564.64714, "r": 545.08014, "b": 573.86014, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "valuable assets, which is the data.", "bbox": {"l": 136.8, "t": 576.64694, "r": 287.80057, "b": 585.85994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company\u2019s most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 679.87833, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"id": 44, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "bbox": {"l": 136.8, "t": 598.6665, "r": 547.28326, "b": 607.8795, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "bbox": {"l": 136.8, "t": 610.6663100000001, "r": 538.6767, "b": 619.8793000000001, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "the menu options controlling what database operations that the user can perform. This ", "bbox": {"l": 136.8, "t": 622.66611, "r": 520.35364, "b": 631.8791, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "bbox": {"l": 136.80002, "t": 634.6659099999999, "r": 502.77115000000003, "b": 643.87891, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "commands. The reason is that in today\u2019s connected world there are a multitude of interfaces ", "bbox": {"l": 136.80002, "t": 646.66571, "r": 545.16492, "b": 655.87871, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "bbox": {"l": 136.80002, "t": 658.66551, "r": 547.23376, "b": 667.87852, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "bbox": {"l": 136.80002, "t": 670.66532, "r": 526.04187, "b": 679.87833, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today\u2019s connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"id": 0, "text": "2 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. 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Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"id": 0, "text": "4 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 72.821999, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 87.840302, "t": 755.538002, "r": 328.72537, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. 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For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 9, "label": "text", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 24, "text": "User with", "bbox": {"l": 180.95911, "t": 383.45612, "r": 209.08017, "b": 389.0784, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 25, "text": "*ALLOBJ access", "bbox": {"l": 170.00624, "t": 390.95251, "r": 220.10355, "b": 396.57480000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 544.30334, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"id": 2, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 541.19006, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "means that users should be given access only to the minimum set of data that is required to ", "bbox": {"l": 136.8, "t": 83.50847999999996, "r": 544.30334, "b": 92.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "perform their job. Often, users with object-level access are given access to row and column ", "bbox": {"l": 136.8, "t": 95.50829999999996, "r": 540.94299, "b": 104.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "values that are beyond what their business task requires because that object-level security ", "bbox": {"l": 136.80002, "t": 107.50811999999996, "r": 538.27454, "b": 116.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "bbox": {"l": 136.80002, "t": 119.50792999999999, "r": 530.23004, "b": 128.72095000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "bbox": {"l": 136.80002, "t": 131.50775, "r": 536.26263, "b": 140.72076000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "only for the employees that they manage.", "bbox": {"l": 136.80002, "t": 143.50757, "r": 319.04318, "b": 152.72058000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"id": 9, "text": "1.3.1", "bbox": {"l": 64.800003, "t": 173.33471999999995, "r": 94.033653, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Existing row and column control", "bbox": {"l": 97.687859, "t": 173.33471999999995, "r": 301.46902, "b": 185.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79999, "t": 199.48870999999997, "r": 541.56738, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"id": 11, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "bbox": {"l": 136.8, "t": 199.48870999999997, "r": 534.90112, "b": 208.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "bbox": {"l": 136.8, "t": 211.48852999999997, "r": 480.47281000000004, "b": 220.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "bbox": {"l": 136.8, "t": 223.48834, "r": 541.56738, "b": 232.70135000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "bbox": {"l": 136.8, "t": 235.48816, "r": 537.39423, "b": 244.70117000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "and System i Navigator.", "bbox": {"l": 136.79999, "t": 247.48798, "r": 242.24352000000002, "b": 256.70099000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.79999, "t": 269.50757, "r": 547.4408, "b": 314.72003, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"id": 16, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "bbox": {"l": 136.79999, "t": 269.50757, "r": 526.88428, "b": 278.72058000000004, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "bbox": {"l": 136.79999, "t": 281.50742, "r": 531.77087, "b": 290.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "bbox": {"l": 136.79999, "t": 293.50723000000005, "r": 547.4408, "b": 302.72021, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "as the amount of data grows and the number of users increases.", "bbox": {"l": 136.79999, "t": 305.50705, "r": 421.86725, "b": 314.72003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79999, "t": 327.52661, "r": 547.23267, "b": 360.73923, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"id": 20, "text": "Even if you are willing to live with these performance and management issues, a user with ", "bbox": {"l": 136.79999, "t": 327.52661, "r": 536.46692, "b": 336.7395900000001, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "bbox": {"l": 136.79999, "t": 339.52643, "r": 547.23267, "b": 348.73941, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "bypass the security controls that are built into an SQL view.", "bbox": {"l": 136.79999, "t": 351.52624999999995, "r": 397.88553, "b": 360.73923, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"id": 23, "text": "Figure 1-2 Existing row and column controls", "bbox": {"l": 136.8, "t": 691.818, "r": 316.44727, "b": 700.142998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": 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612.0, "height": 792.0}, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "VARCHAR(7)", "bbox": {"l": 216.77367999999998, "t": 364.51862, "r": 270.97977, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "Usage setting:", "bbox": {"l": 289.41626, "t": 364.51862, "r": 346.88757, "b": 372.84363, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 375.69394000000005, "r": 293.9397, "b": 383.62292, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "ALLOWED: The user profile is allowed to use the function.", "bbox": {"l": 303.83969, "t": 375.55893, "r": 535.16766, "b": 383.88394, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 386.67395, "r": 293.9397, "b": 394.60294, "coord_origin": "TOPLEFT"}}, {"id": 64, "text": "DENIED: The user profile is not allowed to use the function.", "bbox": {"l": 303.83969, "t": 386.53894, "r": 539.10712, "b": 394.86395, "coord_origin": "TOPLEFT"}}, {"id": 65, "text": "USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 435.84369, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"id": 2, "text": "2.1.6", "bbox": {"l": 64.800003, "t": 71.33471999999995, "r": 94.081459, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Change Function Usage CL command", "bbox": {"l": 97.741661, "t": 71.33471999999995, "r": 335.49551, "b": 83.32275000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 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"cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": 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"TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": 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0.534027099609375, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373470187187195, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 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0.9551582932472229, "cells": [{"id": 4, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "bbox": {"l": 136.8, "t": 97.48870999999997, "r": 547.28442, "b": 106.70172000000014, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"id": 5, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 114.67767000000003, "r": 141.78, "b": 123.45245, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Work Function Usage (", "bbox": {"l": 151.20016, "t": 114.52826000000005, "r": 253.26227000000003, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "WRKFCNUSG", "bbox": {"l": 253.26028, "t": 114.67767000000003, "r": 298.1998, "b": 123.50225999999998, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": ")", "bbox": {"l": 298.20081, "t": 114.52826000000005, "r": 301.51749, "b": 123.74126999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.80099, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"id": 9, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80099, "t": 126.67749000000003, "r": 141.78099, "b": 135.45227, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "Change Function Usage (", "bbox": {"l": 151.20116, "t": 126.52808000000005, "r": 265.13354, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "CHGFCNUSG", "bbox": {"l": 265.08081, "t": 126.67749000000003, "r": 310.02032, "b": 135.50207999999998, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": ")", "bbox": {"l": 310.08109, "t": 126.52808000000005, "r": 313.39777, "b": 135.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.80098, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80098, "t": 138.67731000000003, "r": 141.78098, "b": 147.45209, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Display Function Usage (", "bbox": {"l": 151.20114, "t": 138.52788999999996, "r": 262.5639, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "DSPFCNUSG", "bbox": {"l": 262.56091, "t": 138.67731000000003, "r": 307.50043, "b": 147.50189, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": ")", "bbox": {"l": 307.50043, "t": 138.52788999999996, "r": 310.81711, "b": 147.74090999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 512.53802, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"id": 17, "text": "For example, the following ", "bbox": {"l": 136.79997, "t": 160.48766999999998, "r": 255.09984000000003, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "CHGFCNUSG", "bbox": {"l": 255.00027, "t": 160.63707999999997, "r": 299.99957, "b": 169.46167000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": " command shows granting authorization to user ", "bbox": {"l": 300.00055, "t": 160.48766999999998, "r": 512.53802, "b": 169.70068000000003, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "HBEDOYA to administer and manage RCAC rules:", "bbox": {"l": 136.80096, "t": 172.48748999999998, "r": 360.41989, "b": 181.70050000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"id": 21, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "bbox": {"l": 136.80096, "t": 189.67645000000005, "r": 441.59686, "b": 198.45123, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"id": 22, "text": "2.1.7", "bbox": {"l": 64.800003, "t": 219.35468000000003, "r": 93.757614, "b": 231.34271, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "bbox": {"l": 97.377296, "t": 219.35468000000003, "r": 544.47546, "b": 231.34271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 266.7215, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"id": 24, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "bbox": {"l": 136.8, "t": 245.50867000000005, "r": 519.51794, "b": 254.72168, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "describes the columns in the FUNCTION_USAGE view.", "bbox": {"l": 136.8, "t": 257.50847999999996, "r": 382.94443, "b": 266.7215, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"id": 26, "text": "Table 2-1 FUNCTION_USAGE view", "bbox": {"l": 136.8, "t": 279.55798000000004, "r": 283.96805, "b": 287.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 473.72153, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"id": 27, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "bbox": {"l": 136.8, "t": 452.50872999999996, "r": 547.2804, "b": 461.72171, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "shown in Example 2-1.", "bbox": {"l": 136.8, "t": 464.50854, "r": 237.76951999999997, "b": 473.72153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"id": 29, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "bbox": {"l": 136.8, "t": 486.55798, "r": 462.35419, "b": 494.883, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.65802, "r": 331.67731, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"id": 30, "text": "SELECT", "bbox": {"l": 136.8, "t": 503.65802, "r": 171.26956, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "function_id,", "bbox": {"l": 182.75941, "t": 503.65802, "r": 251.69853, "b": 512.4328, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "user_name,", "bbox": {"l": 166.78244, "t": 515.6578400000001, "r": 241.73852999999997, "b": 524.43262, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "usage,", "bbox": {"l": 170.75961, "t": 527.65765, "r": 221.69901999999996, "b": 536.43242, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"id": 34, "text": "user_type", "bbox": {"l": 167.53809, "t": 539.65747, "r": 236.69878, "b": 548.43222, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.581838846206665, "cells": [{"id": 35, "text": "FROM", "bbox": {"l": 136.8, "t": 551.65727, "r": 160.59396, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"id": 36, "text": "function_usage", "bbox": {"l": 178.43944, "t": 551.65727, "r": 261.71829, "b": 560.43202, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.534027099609375, "cells": [{"id": 37, "text": "WHERE", "bbox": {"l": 136.8, "t": 563.65707, "r": 162.44176, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"id": 38, "text": "function_id=\u2019QIBM_DB_SECADM\u2019", "bbox": {"l": 177.8268, "t": 563.65707, "r": 331.67731, "b": 572.43182, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5373470187187195, "cells": [{"id": 39, "text": "ORDER BY", "bbox": {"l": 136.8, "t": 575.65688, "r": 178.77542, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"id": 40, "text": "user_name;", "bbox": {"l": 189.26929, "t": 575.65688, "r": 241.73856, "b": 584.43163, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.800003, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"id": 41, "text": "2.2", "bbox": {"l": 64.800003, "t": 620.22063, "r": 87.569839, "b": 634.98363, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "Separation of duties", "bbox": {"l": 92.123802, "t": 620.22063, "r": 249.59605000000002, "b": 634.98363, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.54872, "r": 547.22345, "b": 709.760956, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"id": 43, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "bbox": {"l": 136.8, "t": 652.54872, "r": 529.09357, "b": 661.76172, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "bbox": {"l": 136.8, "t": 664.54852, "r": 547.22345, "b": 673.76153, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "bbox": {"l": 136.8, "t": 676.54833, "r": 530.89716, "b": 685.76134, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "bbox": {"l": 136.80002, "t": 688.54814, "r": 544.33832, "b": 697.7611469999999, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "bbox": {"l": 136.80002, "t": 700.547951, "r": 536.28363, "b": 709.760956, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority."}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"id": 48, "text": "Column name", "bbox": {"l": 142.8, "t": 296.5379899999999, "r": 202.245, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Data type", "bbox": {"l": 216.80878999999996, "t": 296.5379899999999, "r": 257.21069, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "Description", "bbox": {"l": 289.47479, "t": 296.5379899999999, "r": 338.89468, "b": 304.86301, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "FUNCTION_ID", "bbox": {"l": 142.8, "t": 315.55771, "r": 203.2323, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "VARCHAR(30)", "bbox": {"l": 216.7854, "t": 315.55771, "r": 276.0036, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ID of the function.", "bbox": {"l": 289.4577, "t": 315.55771, "r": 359.85394, "b": 323.88272, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "USER_NAME", "bbox": {"l": 142.8, "t": 334.51801, "r": 198.6693, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "VARCHAR(10)", "bbox": {"l": 216.74129999999997, "t": 334.51801, "r": 275.92349, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Name of the user profile that has a usage setting for this ", "bbox": {"l": 289.38208, "t": 334.51801, "r": 515.05359, "b": 342.84302, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "function.", "bbox": {"l": 289.4397, "t": 345.55832, "r": 323.43362, "b": 353.88333, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "USAGE", "bbox": {"l": 142.79999, "t": 364.51862, "r": 173.98318, "b": 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"USER_TYPE", "bbox": {"l": 142.8, "t": 405.55865, "r": 196.2249, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 66, "text": "VARCHAR(5)", "bbox": {"l": 216.75211, "t": 405.55865, "r": 270.99872, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 67, "text": "Type of user profile:", "bbox": {"l": 289.43161, "t": 405.55865, "r": 367.8009, "b": 413.88367000000005, "coord_origin": "TOPLEFT"}}, {"id": 68, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 416.67368000000005, "r": 293.9397, "b": 424.60266, "coord_origin": "TOPLEFT"}}, {"id": 69, "text": "USER: The user profile is a user.", "bbox": {"l": 303.83969, "t": 416.53867, "r": 434.78159, "b": 424.86368, "coord_origin": "TOPLEFT"}}, {"id": 70, "text": "GLYPH<SM590000>", "bbox": {"l": 289.4397, "t": 427.65369, "r": 293.9397, "b": 435.58267000000006, "coord_origin": "TOPLEFT"}}, {"id": 71, "text": "GROUP: The user profile is a group.", "bbox": {"l": 303.83969, "t": 427.51868, "r": 448.11963000000003, "b": 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GLYPH<SM590000> GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"id": 0, "text": "10 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 2. Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "bbox": {"l": 64.800003, "t": 383.5379899999999, "r": 391.75464, "b": 391.86301, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "User action", "bbox": {"l": 70.800301, "t": 400.51827999999995, "r": 119.78551, "b": 408.84329, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "*JOBCTL", "bbox": {"l": 424.93805, "t": 447.52255, "r": 433.26297000000005, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "QIBM_DB_SECADM", "bbox": {"l": 450.13806, "t": 401.6000700000001, "r": 458.46298, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "QIBM_DB_SQLADM", "bbox": {"l": 475.93835000000007, "t": 401.53442, "r": 484.26327999999995, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "QIBM_DB_SYSMON", "bbox": {"l": 501.13837, "t": 401.6145, "r": 509.46329, "b": 487.01999, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "No Authority", "bbox": {"l": 526.39862, "t": 432.79944, "r": 534.72357, "b": 487.02005, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "SET CURRENT DEGREE", "bbox": {"l": 70.800003, "t": 498.69299, "r": 151.6794, "b": 506.66699, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " (SQL statement)", "bbox": {"l": 151.6803, "t": 498.55798, "r": 220.15681000000004, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "X", "bbox": {"l": 429.0, "t": 498.55798, "r": 435.00299000000007, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "X", "bbox": {"l": 480.00031, "t": 498.55798, "r": 486.0033, "b": 506.883, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "CHGQRYA", "bbox": {"l": 70.800018, "t": 517.65329, "r": 102.23972, "b": 525.62729, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": " command targeting a different user\u2019s job", "bbox": {"l": 102.23972, "t": 517.51828, "r": 264.5538, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "X", "bbox": {"l": 429.00003, "t": 517.51828, "r": 435.00302000000005, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "X", "bbox": {"l": 480.00034, "t": 517.51828, "r": 486.00333, "b": 525.84329, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 536.67299, "r": 106.73975, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": " or ", "bbox": {"l": 106.73975, "t": 536.5379800000001, "r": 119.77895, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 536.67299, "r": 155.69974, "b": 544.64699, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": " commands targeting a different user\u2019s job", "bbox": {"l": 155.69974, "t": 536.5379800000001, "r": 322.50574, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "X", "bbox": {"l": 429.00003, "t": 536.5379800000001, "r": 435.00302000000005, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "X", "bbox": {"l": 480.00034, "t": 536.5379800000001, "r": 486.00333, "b": 544.8629900000001, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "STRDBMON", "bbox": {"l": 70.800049, "t": 555.69269, "r": 106.73975, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": " or ", "bbox": {"l": 106.73975, "t": 555.55768, "r": 119.77895, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "ENDDBMON", "bbox": {"l": 119.69975000000001, "t": 555.69269, "r": 155.69974, "b": 563.66669, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": " commands targeting a job that matches the current user", "bbox": {"l": 155.69974, "t": 555.55768, "r": 381.02185, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "X", "bbox": {"l": 429.00003, "t": 555.55768, "r": 435.00302000000005, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "X", "bbox": {"l": 480.00034, "t": 555.55768, "r": 486.00333, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "X", "bbox": {"l": 505.26061999999996, "t": 555.55768, "r": 511.26361, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "X", "bbox": {"l": 530.76031, "t": 555.55768, "r": 536.76331, "b": 563.8826899999999, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "QUSRJOBI() API format 900 or System i Navigator\u2019s SQL Details for Job", "bbox": {"l": 70.800049, "t": 574.51797, "r": 359.51736, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "X", "bbox": {"l": 429.0000600000001, "t": 574.51797, "r": 435.00305000000003, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "X", "bbox": {"l": 480.00037, "t": 574.51797, "r": 486.00335999999993, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "X", "bbox": {"l": 505.2606799999999, "t": 574.51797, "r": 511.26367, "b": 582.84299, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "Visual Explain within Run SQL scripts", "bbox": {"l": 70.800079, "t": 593.5376699999999, "r": 220.75178999999997, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "X", "bbox": {"l": 429.0000600000001, "t": 593.5376699999999, "r": 435.00305000000003, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "X", "bbox": {"l": 480.00037, "t": 593.5376699999999, "r": 486.00335999999993, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 59, "text": "X", "bbox": {"l": 505.2606799999999, "t": 593.5376699999999, "r": 511.26367, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 60, "text": "X", "bbox": {"l": 530.76038, "t": 593.5376699999999, "r": 536.76337, "b": 601.8626899999999, "coord_origin": "TOPLEFT"}}, {"id": 61, "text": "Visual Explain outside of Run SQL scripts", "bbox": {"l": 70.800079, "t": 612.55737, "r": 236.6548, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 62, "text": "X", "bbox": {"l": 429.0000600000001, "t": 612.55737, "r": 435.00305000000003, "b": 620.88239, "coord_origin": "TOPLEFT"}}, {"id": 63, "text": "X", "bbox": {"l": 480.00037, "t": 612.55737, "r": 486.00335999999993, "b": 620.88239, 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Roles and separation of duties ", "bbox": {"l": 355.32001, "t": 755.538002, "r": 523.54071, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987301588058472, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i 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Roles and separation of duties"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8987301588058472, "cells": [{"id": 1, "text": "11", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "11"}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"id": 2, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 542.69434, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "bbox": {"l": 136.79959, "t": 83.50885000000017, "r": 513.67804, "b": 92.72185999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "bbox": {"l": 136.79959, "t": 95.50867000000005, "r": 509.71902, "b": 104.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "bbox": {"l": 136.79959, "t": 107.50847999999996, "r": 528.20184, "b": 116.72149999999999, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "bbox": {"l": 136.79959, "t": 119.50829999999996, "r": 531.84015, "b": 128.72131000000002, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "PAYROLL table even though Theresa\u2019s job description was only to manage its security.", "bbox": {"l": 136.79959, "t": 131.50811999999996, "r": 519.24982, "b": 140.72113000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa\u2019s job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 547.30396, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"id": 8, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "bbox": {"l": 136.79959, "t": 153.52770999999996, "r": 544.16064, "b": 162.74072, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "bbox": {"l": 136.79959, "t": 165.52752999999996, "r": 540.66156, "b": 174.74054, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "bbox": {"l": 136.79959, "t": 177.52733999999998, "r": 547.30396, "b": 186.74036, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "table. ", "bbox": {"l": 136.79959, "t": 189.52715999999998, "r": 163.6189, "b": 198.74017000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"id": 12, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "bbox": {"l": 136.79959, "t": 211.48694, "r": 538.65076, "b": 220.69994999999994, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "authority and can be given to a user or a group.", "bbox": {"l": 136.79959, "t": 223.48676, "r": 346.34808, "b": 232.69976999999994, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"id": 14, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "bbox": {"l": 136.79959, "t": 245.50635, "r": 545.79602, "b": 254.71936000000005, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "bbox": {"l": 136.79959, "t": 257.50616, "r": 529.46149, "b": 266.71918000000005, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "certain columns of a table.", "bbox": {"l": 136.79959, "t": 269.50598, "r": 253.47696999999997, "b": 278.71898999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.79959, "t": 291.52557, "r": 539.80713, "b": 336.73801, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"id": 17, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "bbox": {"l": 136.79959, "t": 291.52557, "r": 533.78137, "b": 300.73856, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "bbox": {"l": 136.79959, "t": 303.52539, "r": 539.80713, "b": 312.73837000000003, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "bbox": {"l": 136.79959, "t": 315.52521, "r": 534.5741, "b": 324.73819, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "access to data itself.", "bbox": {"l": 136.79959, "t": 327.52502, "r": 227.02324, "b": 336.73801, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"id": 21, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "bbox": {"l": 136.79959, "t": 349.48483, "r": 543.06714, "b": 358.69780999999995, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "the different CL commands and DB2 for i tools.", "bbox": {"l": 136.79959, "t": 361.48465, "r": 343.79236, "b": 370.69763000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 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Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 566.15842, "r": 191.70256, "b": 574.66917, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 578.10333, "r": 232.56117, "b": 586.61409, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1737060546875, "r": 301.2367248535156, "b": 594.7566528320312, "coord_origin": "TOPLEFT"}, "confidence": 0.7221468687057495, "cells": [{"id": 38, "text": "SignedonasALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 218.71170000000004, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "Signed on as ALICE", "bbox": {"l": 140.7323, "t": 386.98453, "r": 216.40009, "b": 395.49527, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "USER = ALICE", "bbox": {"l": 138.476, "t": 410.87441999999993, "r": 191.70256, "b": 419.38516, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "CURRENT USER = ALICE", "bbox": {"l": 138.476, "t": 422.81934, "r": 232.56117, "b": 431.33008, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CALL proc1", "bbox": {"l": 138.476, "t": 446.70923000000005, "r": 183.26944, "b": 455.21997, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "P1", "bbox": {"l": 148.4301, "t": 473.58524, "r": 184.17328, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "Proc1:", "bbox": {"l": 148.4301, "t": 473.58524, "r": 174.05859, "b": 482.09598, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "Owner = JOE", "bbox": {"l": 157.52185, "t": 485.53015, "r": 209.103, "b": 494.04089, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "SET OPTION USRPRF=*OWNER", "bbox": {"l": 157.52185, "t": 497.47507, "r": 281.68927, "b": 505.98581, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "USER = ALICE", "bbox": {"l": 148.4301, "t": 521.36493, "r": 201.65666, "b": 529.87567, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "CURRENT USER = JOE", "bbox": {"l": 148.4301, "t": 533.30984, "r": 234.57686999999999, "b": 541.82059, "coord_origin": 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"used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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"TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"id": 2, "text": "Table 3-1 summarizes these special registers and their values.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 412.20758, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"id": 3, "text": "Table 3-1 Special registers and their corresponding values", "bbox": {"l": 136.8, "t": 93.49805000000003, "r": 372.60364, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"id": 4, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "bbox": {"l": 136.8, "t": 214.48870999999997, "r": 538.4939, "b": 223.70172000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "used:", "bbox": {"l": 136.8, "t": 226.48852999999997, "r": 161.20995, "b": 235.70154000000002, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"id": 6, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 243.67749000000003, "r": 141.78, "b": 252.45227, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "A user connects to the server using the user profile ALICE.", "bbox": {"l": 151.20016, "t": 243.52808000000005, "r": 411.36139, "b": 252.74108999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"id": 8, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 260.65729, "r": 141.78, "b": 269.43206999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "USER and CURRENT USER initially have the same value of ALICE.", "bbox": {"l": 151.20016, "t": 260.50787, "r": 453.2580899999999, "b": 269.72089000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48767, "r": 541.44983, "b": 298.7005, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"id": 10, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 277.63707999999997, "r": 141.78, "b": 286.41187, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "bbox": {"l": 151.20016, "t": 277.48767, "r": 541.44983, "b": 286.70068, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "and was created to adopt JOE\u2019s authority when it is called.", "bbox": {"l": 151.20016, "t": 289.4875200000001, "r": 409.82953, "b": 298.7005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE\u2019s authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.5271, "r": 547.21674, "b": 339.73972, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"id": 13, "text": "GLYPH<SM590000>", "bbox": {"l": 136.8, "t": 306.67647999999997, "r": 141.78, "b": 315.45126000000005, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "bbox": {"l": 151.20016, "t": 306.5271, "r": 547.21674, "b": 315.74008, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "bbox": {"l": 151.20117, "t": 318.52691999999996, "r": 514.32971, "b": 327.7399, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "contains the value of JOE because it includes any adopted authority.", "bbox": {"l": 151.20117, "t": 330.52673, "r": 453.3249200000001, "b": 339.73972, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.80101, "t": 347.50653, "r": 547.35406, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"id": 17, "text": "GLYPH<SM590000>", "bbox": {"l": 136.80101, "t": 347.65591, "r": 141.78101, "b": 356.43069, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "bbox": {"l": 151.20117, "t": 347.50653, "r": 547.35406, "b": 356.71950999999996, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "USER having the value of ALICE.", "bbox": {"l": 151.20117, "t": 359.50635, "r": 299.57532, "b": 368.71933000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "GLYPH<SM590000> When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"id": 20, "text": "Figure 3-5 Special registers and adopted authority", "bbox": {"l": 136.8, "t": 596.7179, "r": 341.25662, "b": 605.04291, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.800003, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"id": 21, "text": "3.2.2", "bbox": {"l": 64.800003, "t": 625.55472, "r": 94.20356, "b": 637.54272, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Built-in global variables", "bbox": {"l": 97.879005, "t": 625.55472, "r": 247.02536, "b": 637.54272, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 672.92153, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"id": 23, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "bbox": {"l": 136.8, "t": 651.70872, "r": 518.00116, "b": 660.92172, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "statements to retrieve scalar values that are associated with the variables.", "bbox": {"l": 136.8, "t": 663.70853, "r": 462.81759999999997, "b": 672.92153, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.7281, "r": 532.3385, "b": 718.94072, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"id": 25, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "bbox": {"l": 136.8, "t": 685.7281, "r": 504.44669, "b": 694.941101, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "bbox": {"l": 136.8, "t": 697.727905, "r": 532.3385, "b": 706.94091, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "database connection and used as part of the RCAC logic.", "bbox": {"l": 136.8, "t": 709.727715, "r": 391.38257, "b": 718.94072, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": [{"id": 26, "label": "text", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 28, "text": "Special register", "bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Corresponding value", "bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "USER or", "bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 178.26361, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 31, "text": "SESSION_USER", "bbox": {"l": 142.80002, "t": 140.53864, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 30, "label": "text", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 32, "text": "The effective user of the thread excluding adopted authority.", "bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 33, "text": "CURRENT_USER", "bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 34, "text": "The effective user of the thread including adopted authority. When no adopted ", "bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 35, "text": "authority is present, this has the same value as USER.", "bbox": {"l": 230.22061, "t": 170.53832999999997, "r": 447.36533, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 36, "text": "SYSTEM_USER", "bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 37, "text": "The authorization ID that initiated the connection.", "bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53801999999985, "r": 209.67091, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.18912000000003, "t": 110.53801999999985, "r": 319.93527, "b": 118.86298, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80002, "t": 129.49834999999996, "r": 212.70122, "b": 148.86359000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.21973000000003, "t": 129.49834999999996, "r": 467.99069000000003, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.80003, "t": 159.55835000000002, "r": 216.63962999999998, "b": 167.88329999999996, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.19814, "t": 159.55835000000002, "r": 535.65082, "b": 178.86328000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. When no adopted  authority is present, this has the same value as USER.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8009, "t": 189.55804, "r": 209.7357, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "SYSTEM_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.2449, "t": 189.55804, "r": 425.64569, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The authorization ID that initiated the connection.", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "picture", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 135.64837646484375, "t": 384.1737060546875, 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"confidence": 0.9557498693466187, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"id": 1, "text": "19", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CLIENT_HOST", "bbox": {"l": 70.800003, "t": 129.49834999999996, "r": 132.7209, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "VARCHAR(255)", "bbox": {"l": 202.89029, "t": 129.49834999999996, "r": 267.07651, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Host name of the current client as returned by the system", "bbox": {"l": 281.84732, "t": 129.49834999999996, "r": 510.17548, "b": 137.82330000000002, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "CLIENT_IPADDR", "bbox": {"l": 70.800018, "t": 148.51806999999997, "r": 140.66522, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "VARCHAR(128)", "bbox": {"l": 202.87231, "t": 148.51806999999997, "r": 267.07739, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "IP address of the current client as returned by the system", "bbox": {"l": 281.84549, "t": 148.51806999999997, "r": 509.60583, "b": 156.84302000000002, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ROUTINE_SCHEMA", "bbox": {"l": 70.800018, "t": 243.55724999999995, "r": 154.41992, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "VARCHAR(128)", "bbox": {"l": 202.79312, "t": 243.55724999999995, "r": 267.09274, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "Schema name of the currently running routine", "bbox": {"l": 281.87164, "t": 243.55724999999995, "r": 464.26022, "b": 251.8822, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ROUTINE_SPECIFIC_NAME", "bbox": {"l": 70.800018, "t": 262.51757999999995, "r": 188.43991, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "VARCHAR(128)", "bbox": {"l": 202.84441, "t": 262.51757999999995, "r": 267.03693, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 55, "text": "Name of the currently running routine", "bbox": {"l": 281.80682, "t": 262.51757999999995, "r": 430.40045, "b": 270.84253, "coord_origin": "TOPLEFT"}}, {"id": 56, "text": "ROUTINE_TYPE", "bbox": {"l": 70.800034, "t": 281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": 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342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Description", "bbox": {"l": 281.8248, "t": 110.53801999999985, "r": 331.3428, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 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"TOPLEFT"}}, {"id": 38, "text": "CLIENT_PORT ", "bbox": {"l": 70.800018, "t": 167.53778, "r": 134.98264, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "INTEGER", "bbox": {"l": 202.90294, "t": 167.53778, "r": 242.80084, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "Port used by the current client to communicate with the server", "bbox": {"l": 281.79785, "t": 167.53778, "r": 527.59222, "b": 175.86273000000006, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "PACKAGE_NAME", "bbox": {"l": 70.800018, "t": 186.5575, "r": 143.50925, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "VARCHAR(128)", "bbox": {"l": 202.80576, "t": 186.5575, "r": 267.06937, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "Name of the currently running package", "bbox": {"l": 281.85187, "t": 186.5575, "r": 436.57259999999997, "b": 194.88244999999995, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "PACKAGE_SCHEMA", "bbox": {"l": 70.800018, "t": 205.51782000000003, "r": 156.01654, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "VARCHAR(128)", "bbox": {"l": 202.83545, "t": 205.51782000000003, "r": 267.08646, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "Schema name of the currently running package", "bbox": {"l": 281.87076, "t": 205.51782000000003, "r": 470.44678, "b": 213.84276999999997, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "PACKAGE_VERSION", "bbox": {"l": 70.800018, "t": 224.53754000000004, "r": 157.89932, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "VARCHAR(64)", "bbox": {"l": 202.72472, "t": 224.53754000000004, "r": 261.98254, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "Version identifier of the currently running package", "bbox": {"l": 281.74924, "t": 224.53754000000004, "r": 478.8438100000001, "b": 232.86248999999998, "coord_origin": "TOPLEFT"}}, {"id": 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281.53726, "r": 139.43135, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 57, "text": "CHAR(1)", "bbox": {"l": 202.74635, "t": 281.53726, "r": 239.28996000000004, "b": 289.86227, "coord_origin": "TOPLEFT"}}, {"id": 58, "text": "Type of the currently running routine", "bbox": {"l": 281.79065, "t": 281.53726, "r": 425.09131, "b": 289.86227, "coord_origin": "TOPLEFT"}}], "children": [{"id": 14, "label": "text", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 30, "text": "Type", "bbox": {"l": 202.8894, "t": 110.53801999999985, "r": 223.34641, "b": 118.86298, 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10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"id": 2, "text": "Table 3-2 lists the nine built-in global variables.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 342.54773, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"id": 3, "text": "Table 3-2 Built-in global variables", "bbox": {"l": 64.800003, "t": 93.49805000000003, "r": 201.18147, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.800003, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"id": 4, "text": "3.3", "bbox": {"l": 64.800003, "t": 322.20071, "r": 87.318192, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "VERIFY_GROUP_FOR_USER function", "bbox": {"l": 91.821815, "t": 322.20071, "r": 384.36389, "b": 336.96371000000005, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.52872, "r": 547.23474, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"id": 6, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "bbox": {"l": 136.8, "t": 354.52872, "r": 542.83539, "b": 363.7417, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "bbox": {"l": 136.8, "t": 366.52853, "r": 547.14783, "b": 375.74152, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "bbox": {"l": 136.8, "t": 378.52835, "r": 547.15106, "b": 387.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "bbox": {"l": 136.80002, "t": 390.52817, "r": 520.62958, "b": 399.74115000000006, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "bbox": {"l": 136.80002, "t": 402.52798, "r": 524.88824, "b": 411.74097, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "bbox": {"l": 136.80002, "t": 414.5278, "r": 547.23474, "b": 423.7407799999999, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "do not exist without receiving any kind of error.", "bbox": {"l": 136.80002, "t": 426.52762, "r": 342.04672, "b": 435.74060000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.80002, "t": 448.48743, "r": 547.25739, "b": 481.70004, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"id": 13, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "bbox": {"l": 136.80002, "t": 448.48743, "r": 525.1474, "b": 457.70041, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "bbox": {"l": 136.80002, "t": 460.48724, "r": 547.25739, "b": 469.70023, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "of 0. It never returns the null value.", "bbox": {"l": 136.80002, "t": 472.48706, "r": 289.84335, "b": 481.70004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"id": 16, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "bbox": {"l": 136.80002, "t": 494.50662, "r": 458.44525000000004, "b": 503.7196, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.80002, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"id": 17, "text": "1.", "bbox": {"l": 136.80002, "t": 511.5462, "r": 145.09804, "b": 520.75919, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "bbox": {"l": 147.86403, "t": 511.5462, "r": 406.07751, "b": 520.75919, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"id": 19, "text": "2.", "bbox": {"l": 136.80002, "t": 528.5260000000001, "r": 145.23297, "b": 537.739, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "The user profile JANE specifies a group profile of MGR.", "bbox": {"l": 148.04396, "t": 528.5260000000001, "r": 396.98816, "b": 537.739, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.80002, "t": 545.50581, "r": 536.5686, "b": 566.71861, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"id": 21, "text": "3.", "bbox": {"l": 136.80002, "t": 545.50581, "r": 145.18951, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "bbox": {"l": 147.98601, "t": 545.50581, "r": 536.5686, "b": 554.71881, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "invocations return a value of 1:", "bbox": {"l": 151.20018, "t": 557.50562, "r": 286.84641, "b": 566.71861, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.20018, "t": 574.69458, "r": 451.01605, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"id": 24, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "bbox": {"l": 151.20018, "t": 574.69458, "r": 366.05725, "b": 583.46933, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "bbox": {"l": 151.20018, "t": 586.69438, "r": 406.01678, "b": 595.46913, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "bbox": {"l": 151.20018, "t": 598.69418, "r": 451.01605, "b": 607.46893, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "The following function invocation returns a value of 0:", "bbox": {"l": 151.20018, "t": 615.5246, "r": 385.87271, "b": 624.73759, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "bbox": {"l": 151.20018, "t": 632.65381, "r": 411.05655, "b": 641.4285600000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"id": 29, "text": "Global variable", "bbox": {"l": 70.800003, "t": 110.53801999999985, "r": 134.99071, "b": 118.86298, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "Type", "bbox": 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running routine", "column_header": false, "row_header": false, "row_section": false}]}], "headers": [{"label": "page_footer", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9187920093536377, "cells": [{"id": 0, "text": "20 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"id": 2, "text": "RETURN", "bbox": {"l": 136.79959, "t": 71.65845000000002, "r": 166.73935, "b": 80.43322999999998, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"id": 3, "text": "CASE", "bbox": {"l": 136.79959, "t": 83.65826000000004, "r": 156.77934, "b": 92.43304, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.79959, "t": 95.65808000000015, "r": 521.57428, "b": 260.43048, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"id": 4, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "bbox": {"l": 147.26993, "t": 95.65808000000015, "r": 466.61502, "b": 104.43286000000012, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 107.65790000000004, "r": 311.69717, "b": 116.43268, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 131.65752999999995, "r": 436.61547999999993, "b": 140.43231000000003, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "AND SESSION_USER = EMPLOYEES . USER_ID", "bbox": {"l": 147.54245, "t": 143.65734999999995, "r": 351.65668, "b": 152.43213000000003, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "THEN EMPLOYEES . DATE_OF_BIRTH", "bbox": {"l": 147.73068, "t": 155.65716999999995, "r": 311.69717, "b": 164.43195000000003, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 147.31944, "t": 179.65679999999998, "r": 436.61547999999993, "b": 188.43158000000005, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID", "bbox": {"l": 147.52335, "t": 191.65661999999998, "r": 356.63669, "b": 200.43140000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "THEN ( 9999 || '-' ||", "bbox": {"l": 147.63832, "t": 203.65643, "r": 261.44492, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-'", "bbox": {"l": 272.28363, "t": 203.65643, "r": 499.89682, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "||", "bbox": {"l": 510.73557, "t": 203.65643, "r": 521.57428, "b": 212.43120999999996, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "DAY (EMPLOYEES.DATE_OF_BIRTH ))", "bbox": {"l": 160.78555, "t": 215.65625, "r": 346.6767, "b": 224.43102999999996, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "ELSE NULL", "bbox": {"l": 149.51941, "t": 227.65607, "r": 206.75861, "b": 236.43084999999996, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": " END", "bbox": {"l": 136.79959, "t": 239.65588000000002, "r": 156.77934, "b": 248.43066, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": " ENABLE ;", "bbox": {"l": 136.79959, "t": 251.65570000000002, "r": 186.7191, "b": 260.43048, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 547.21222, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"id": 18, "text": "2.", "bbox": {"l": 136.79959, "t": 275.50591999999995, "r": 145.18994, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "The other column to mask in this example is the TAX_ID information. In this example, the ", "bbox": {"l": 147.98672, "t": 275.50591999999995, "r": 547.21222, "b": 284.7189, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "rules to enforce include the following ones:", "bbox": {"l": 151.19977, "t": 287.50574, "r": 339.37903, "b": 296.71871999999996, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"id": 21, "text": "-", "bbox": {"l": 152.0394, "t": 304.48553000000004, "r": 157.61201, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "bbox": {"l": 165.59894, "t": 304.48553000000004, "r": 469.1528, "b": 313.69852000000003, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.0394, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"id": 23, "text": "-", "bbox": {"l": 152.0394, "t": 321.52512, "r": 157.60504, "b": 330.7381, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Employees can see only their own unmasked TAX_ID.", "bbox": {"l": 165.59894, "t": 321.52512, "r": 403.95953, "b": 330.7381, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.0394, "t": 338.50491, "r": 545.16846, "b": 359.71771, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"id": 25, "text": "-", "bbox": {"l": 152.0394, "t": 338.50491, "r": 157.57019, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "bbox": {"l": 165.59894, "t": 338.50491, "r": 545.16846, "b": 347.7179, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "the X character (for example, XXX-XX-1234).", "bbox": {"l": 165.59894, "t": 350.50473, "r": 364.67947, "b": 359.71771, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.0394, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"id": 28, "text": "-", "bbox": {"l": 152.0394, "t": 367.48453, "r": 157.59309, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "bbox": {"l": 165.59995, "t": 367.48453, "r": 529.46362, "b": 376.6975100000001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"id": 30, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "bbox": {"l": 151.19978, "t": 384.52411, "r": 530.0603, "b": 393.73709, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"id": 31, "text": "Example 3-9 Creating a mask on the TAX_ID column", "bbox": {"l": 136.8, "t": 406.51801, "r": 351.9873, "b": 414.84302, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 526.55469, "b": 684.44884, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"id": 32, "text": "CREATE MASK", "bbox": {"l": 136.8, "t": 423.67810000000003, "r": 192.91296, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "bbox": {"l": 203.11533, "t": 423.67810000000003, "r": 381.65659, "b": 432.45287999999994, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "ON", "bbox": {"l": 136.8, "t": 435.67792, "r": 148.54184, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "bbox": {"l": 177.89645, "t": 435.67792, "r": 371.63684, "b": 444.45270000000005, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "FOR COLUMN", "bbox": {"l": 136.8, "t": 447.67773, "r": 199.25916, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "TAX_ID", "bbox": {"l": 211.75098, "t": 447.67773, "r": 249.22647, "b": 456.45251, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "RETURN ", "bbox": {"l": 136.8, "t": 459.67755, "r": 176.75952, "b": 468.45233, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "CASE ", "bbox": {"l": 136.8, "t": 471.67737, "r": 161.75977, "b": 480.45215, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "bbox": {"l": 152.84189, "t": 483.67719, "r": 441.59589, "b": 492.45197, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 495.677, "r": 291.7178, "b": 504.45178, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "bbox": {"l": 152.80757, "t": 519.67661, "r": 451.6156, "b": 528.45139, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "AND SESSION_USER = EMPLOYEES . USER_ID ", "bbox": {"l": 153.21835, "t": 531.67642, "r": 366.65683, "b": 540.45117, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "THEN EMPLOYEES . TAX_ID", "bbox": {"l": 154.09363, "t": 543.6762200000001, "r": 286.67804, "b": 552.45097, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "bbox": {"l": 152.82599, "t": 567.67583, "r": 446.63561999999996, "b": 576.45058, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "AND SESSION_USER <> EMPLOYEES . USER_ID ", "bbox": {"l": 153.18398, "t": 579.67563, "r": 371.63684, "b": 588.45038, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "bbox": {"l": 152.60088, "t": 591.67543, "r": 526.55469, "b": 600.45018, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "bbox": {"l": 152.80757, "t": 615.67505, "r": 451.6156, "b": 624.4498, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "THEN EMPLOYEES . TAX_ID ", "bbox": {"l": 154.01309, "t": 627.67485, "r": 291.7178, "b": 636.4496, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 154.5134, "t": 651.67447, "r": 266.69827, "b": 660.44922, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "END", "bbox": {"l": 136.8, "t": 663.67427, "r": 157.7877, "b": 672.44904, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ENABLE ;", "bbox": {"l": 136.8, "t": 675.67409, "r": 181.73952, "b": 684.44884, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"id": 0, "text": "Chapter 3. Row and Column Access Control ", "bbox": {"l": 344.94, "t": 755.538002, "r": 523.60162, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"id": 1, "text": "27", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"id": 2, "text": "3.", "bbox": {"l": 136.8, "t": 71.50867000000005, "r": 145.22156, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "bbox": {"l": 148.02872, "t": 71.50867000000005, "r": 449.9523899999999, "b": 80.72167999999999, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"id": 4, "text": "Figure 3-10 Column masks shown in System i Navigator", "bbox": {"l": 64.800003, "t": 173.53801999999996, "r": 293.13809, "b": 181.86298, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.800003, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"id": 5, "text": "3.6.6", "bbox": {"l": 64.800003, "t": 202.37469, "r": 94.275139, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "Activating RCAC", "bbox": {"l": 97.959534, "t": 202.37469, "r": 203.98521, "b": 214.36273000000006, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"id": 7, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "bbox": {"l": 136.8, "t": 228.52868999999998, "r": 537.09131, "b": 237.74170000000004, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "bbox": {"l": 136.8, "t": 240.5285, "r": 529.20422, "b": 249.74152000000004, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "bbox": {"l": 136.8, "t": 252.52832, "r": 547.22565, "b": 261.74132999999995, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"id": 10, "text": "1.", "bbox": {"l": 136.8, "t": 269.50811999999996, "r": 145.32378, "b": 278.72113, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Run the SQL statements that are shown in Example 3-10.", "bbox": {"l": 148.16501, "t": 269.50811999999996, "r": 409.47888, "b": 278.72113, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"id": 12, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "bbox": {"l": 136.8, "t": 291.55798, "r": 375.29099, "b": 299.88300000000004, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"id": 13, "text": "/*", "bbox": {"l": 136.8, "t": 308.65811, "r": 147.22942, "b": 317.43289, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "Active Row Access Control (permissions) */", "bbox": {"l": 157.65884, "t": 308.65811, "r": 376.67661, "b": 317.43289, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"id": 15, "text": "/*", "bbox": {"l": 136.8, "t": 320.65793, "r": 147.70349, "b": 329.43271, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "Active Column Access Control (masks)", "bbox": {"l": 158.60696, "t": 320.65793, "r": 354.86963, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "/* Active Column Access Control (masks)"}, {"label": "text", "id": 16, "page_no": 13, "cluster": {"id": 16, "label": "text", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 17, "text": "*/", "bbox": {"l": 365.77313, "t": 320.65793, "r": 376.67661, "b": 329.43271, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "*/"}, {"label": "text", "id": 17, "page_no": 13, "cluster": {"id": 17, "label": "text", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 18, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "bbox": {"l": 136.8, "t": 332.65775, "r": 291.7178, "b": 341.43253, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 18, "page_no": 13, "cluster": {"id": 18, "label": "text", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 19, "text": "ACTIVATE ROW ACCESS CONTROL", "bbox": {"l": 136.8, "t": 344.65756, "r": 271.67831, "b": 353.43234000000007, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"id": 20, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "bbox": {"l": 136.8, "t": 356.65738, "r": 291.7178, "b": 365.43216, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.5076, "r": 540.80145, "b": 413.72021, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"id": 21, "text": "2.", "bbox": {"l": 136.8, "t": 380.5076, "r": 145.14954, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "bbox": {"l": 147.93271, "t": 380.5076, "r": 540.80145, "b": 389.72058, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "the main navigation pane of System i Navigator, click ", "bbox": {"l": 151.20013, "t": 392.50742, "r": 387.36169, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "Schemas", "bbox": {"l": 387.29993, "t": 392.50742, "r": 431.07614, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "\uf0ae", "bbox": {"l": 433.85992000000005, "t": 389.64889999999997, "r": 443.69043, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "HR_SCHEMA", "bbox": {"l": 446.51906999999994, "t": 392.50742, "r": 509.73618000000005, "b": 401.7203999999999, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "\uf0ae", "bbox": {"l": 512.5788, "t": 389.64889999999997, "r": 522.4093, "b": 401.83994, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Tables", "bbox": {"l": 151.19812, "t": 404.50723000000005, "r": 181.12892, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": ", right-click the ", "bbox": {"l": 181.79823, "t": 404.50723000000005, "r": 248.99638, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "EMPLOYEES", "bbox": {"l": 248.93860000000004, "t": 404.50723000000005, "r": 310.44357, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": " table, and click ", "bbox": {"l": 310.49835, "t": 404.50723000000005, "r": 381.53305, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "Definition", "bbox": {"l": 381.59882, "t": 404.50723000000005, "r": 427.68176, "b": 413.72021, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": ".", "bbox": {"l": 427.67877, "t": 404.50723000000005, "r": 430.4476599999999, "b": 413.72021, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"id": 34, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "bbox": {"l": 64.800003, "t": 649.0378900000001, "r": 347.43054, "b": 657.3629, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"id": 0, "text": "28 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 78.402, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 93.420303, "t": 755.538002, "r": 334.42142, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "bbox": {"l": 214.8, "t": 755.538002, "r": 523.59357, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "2.", "bbox": {"l": 136.79959, "t": 71.50903000000005, "r": 145.19554, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "bbox": {"l": 147.9942, "t": 71.50903000000005, "r": 513.35919, "b": 80.72204999999985, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "enabled. 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Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"id": 1, "text": "77", "bbox": {"l": 536.09998, "t": 754.848721, "r": 547.25916, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 500.69727, "b": 706.60762, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"id": 2, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 71.67296999999996, "r": 177.1194, "b": 79.60199, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 82.65295000000015, "r": 365.87817, "b": 90.58196999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "bbox": {"l": 64.800308, "t": 93.63292999999999, "r": 392.81787, "b": 101.56195000000002, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 104.67322000000001, "r": 374.87817, "b": 112.60222999999996, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "THEN C . CUSTOMER_TAX_ID ", "bbox": {"l": 64.800308, "t": 115.65319999999997, "r": 177.1194, "b": 123.58220999999992, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "ELSE 'XXX-XX-XXXX' ", "bbox": {"l": 64.800308, "t": 126.63318000000015, "r": 150.1797, "b": 134.56219, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "END ", "bbox": {"l": 64.800308, "t": 137.67345999999998, "r": 96.240005, "b": 145.60248, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 148.65344000000005, "r": 124.14001, "b": 156.58245999999997, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 170.67377, "r": 460.25757, "b": 178.60278000000005, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 79.20031, "t": 181.65374999999995, "r": 272.45911, "b": 189.58276, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 192.69403, "r": 137.64001, "b": 200.62305000000003, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 203.67400999999995, "r": 361.37817, "b": 211.60303, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 214.65399000000002, "r": 249.0591, "b": 222.58300999999994, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "bbox": {"l": 64.800308, "t": 225.69426999999996, "r": 365.87817, "b": 233.62329, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 236.67426, "r": 249.0591, "b": 244.60326999999995, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 247.65423999999996, "r": 374.87817, "b": 255.58325000000002, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "bbox": {"l": 64.800308, "t": 258.69452, "r": 249.0591, "b": 266.62354000000005, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "ELSE '*************' ", "bbox": {"l": 64.800308, "t": 269.67449999999997, "r": 159.1797, "b": 277.60352, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "END ", "bbox": {"l": 64.800308, "t": 280.65454, "r": 96.240005, "b": 288.58353, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 291.69485000000003, "r": 124.14001, "b": 299.62384, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 313.65485, "r": 428.81786999999997, "b": 321.58383, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "bbox": {"l": 79.20031, "t": 324.69516, "r": 209.51941, "b": 332.62415, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 335.67517, "r": 137.64001, "b": 343.6041599999999, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 346.65518, "r": 361.37817, "b": 354.58417, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 357.69550000000004, "r": 186.1194, "b": 365.62448, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 368.67551, "r": 374.87817, "b": 376.60449, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "THEN C . CUSTOMER_LOGIN_ID ", "bbox": {"l": 64.800308, "t": 379.65552, "r": 186.1194, "b": 387.5845, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 390.69583, "r": 123.24001, "b": 398.62482, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "END ", "bbox": {"l": 64.800308, "t": 401.67584, "r": 96.240005, "b": 409.60482999999994, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 412.65585, "r": 124.14001, "b": 420.58484, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 434.67615, "r": 469.25757, "b": 442.60513, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 79.20031, "t": 445.65616000000006, "r": 249.95911, "b": 453.58514, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 456.6964699999999, "r": 137.64001, "b": 464.62546, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 467.67648, "r": 361.37817, "b": 475.60547, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 478.65649, "r": 226.5591, "b": 486.58548, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 489.69681, "r": 374.87817, "b": 497.62579, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "bbox": {"l": 64.800308, "t": 500.67682, "r": 226.5591, "b": 508.6058, "coord_origin": "TOPLEFT"}}, {"id": 39, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 511.65683, "r": 123.24001, "b": 519.58582, "coord_origin": "TOPLEFT"}}, {"id": 40, "text": "END ", "bbox": {"l": 64.800308, "t": 522.69714, "r": 96.240005, "b": 530.62613, "coord_origin": "TOPLEFT"}}, {"id": 41, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 533.6771200000001, "r": 124.14001, "b": 541.60614, "coord_origin": "TOPLEFT"}}, {"id": 42, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "bbox": {"l": 64.800308, "t": 555.69745, "r": 500.69727, "b": 563.62645, "coord_origin": "TOPLEFT"}}, {"id": 43, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 79.20031, "t": 566.6774399999999, "r": 281.3988, "b": 574.60645, "coord_origin": "TOPLEFT"}}, {"id": 44, "text": "RETURN CASE ", "bbox": {"l": 79.20031, "t": 577.65744, "r": 137.64001, "b": 585.58644, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "bbox": {"l": 64.800308, "t": 588.6977400000001, "r": 361.37817, "b": 596.62674, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 599.67773, "r": 258.05908, "b": 607.60674, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "bbox": {"l": 64.800308, "t": 610.65773, "r": 374.87817, "b": 618.58673, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "bbox": {"l": 64.800308, "t": 621.69803, "r": 258.05908, "b": 629.62703, "coord_origin": "TOPLEFT"}}, {"id": 49, "text": "ELSE '*****' ", "bbox": {"l": 64.800308, "t": 632.6780200000001, "r": 123.24001, "b": 640.60703, "coord_origin": "TOPLEFT"}}, {"id": 50, "text": "END ", "bbox": {"l": 64.800308, "t": 643.71832, "r": 96.240005, "b": 651.64732, "coord_origin": "TOPLEFT"}}, {"id": 51, "text": "ENABLE ; ", "bbox": {"l": 79.20031, "t": 654.69832, "r": 124.14001, "b": 662.62732, "coord_origin": "TOPLEFT"}}, {"id": 52, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "bbox": {"l": 64.800308, "t": 676.71861, "r": 226.5591, "b": 684.64761, "coord_origin": "TOPLEFT"}}, {"id": 53, "text": "ACTIVATE ROW ACCESS CONTROL ", "bbox": {"l": 79.20031, "t": 687.69862, "r": 214.01941, "b": 695.627617, "coord_origin": "TOPLEFT"}}, {"id": 54, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "bbox": {"l": 79.20031, "t": 698.678619, "r": 223.01941, "b": 706.60762, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"id": 0, "text": "124 ", "bbox": {"l": 64.800003, "t": 754.848721, "r": 83.982002, "b": 764.06172, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"id": 1, "text": "Row and Column Access Control Support in IBM DB2 for i", "bbox": {"l": 98.940002, "t": 755.538002, "r": 339.81958, "b": 763.863001, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 16, "size": {"width": 612.0, "height": 792.0}, "cells": [], "predictions": {"layout": {"clusters": [{"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497200131416321, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497200131416321, "cells": [], "children": []}, "text": null}], "body": [{"label": "form", "id": 0, "page_no": 16, "cluster": {"id": 0, "label": "form", "bbox": {"l": 0.4932013750076294, "t": 0.0, "r": 610.2305297851562, "b": 791.654541015625, "coord_origin": "TOPLEFT"}, "confidence": 0.6497200131416321, "cells": [], "children": []}, "text": null}], "headers": []}}, {"page_no": 17, "size": {"width": 612.0, "height": 792.0}, "cells": [{"id": 0, "text": "fi", "bbox": {"l": 558.11987, "t": 45.468689999999924, "r": 565.46039, "b": 54.68169999999998, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "INTERNATIONAL ", "bbox": {"l": 467.3399999999999, "t": 247.71831999999995, "r": 559.80933, "b": 260.16052, "coord_origin": "TOPLEFT"}}, {"id": 3, "text": "TECHNICAL", "bbox": {"l": 467.3399999999999, "t": 261.75842, "r": 529.50208, "b": 274.20061999999996, "coord_origin": "TOPLEFT"}}, {"id": 4, "text": "SUPPORT", "bbox": {"l": 467.3399999999999, "t": 275.73839999999996, "r": 518.93317, "b": 288.1806, "coord_origin": "TOPLEFT"}}, {"id": 5, "text": "ORGANIZATION", "bbox": {"l": 467.3399999999999, "t": 289.71841, "r": 550.7475, "b": 302.16061, "coord_origin": "TOPLEFT"}}, {"id": 6, "text": "BUILDING TECHNICAL ", "bbox": {"l": 467.3399999999999, "t": 351.79199, "r": 571.70758, "b": 362.47198, "coord_origin": "TOPLEFT"}}, {"id": 7, "text": "INFORMATION BASED ON ", "bbox": {"l": 467.3399999999999, "t": 363.79199, "r": 587.38916, "b": 374.47198, "coord_origin": "TOPLEFT"}}, {"id": 8, "text": "PRACTICAL EXPERIENCE", "bbox": {"l": 467.3399999999999, "t": 375.79199, "r": 582.5556, "b": 386.47198, "coord_origin": "TOPLEFT"}}, {"id": 9, "text": "IBM Redbooks are developed ", "bbox": {"l": 467.3399999999999, "t": 399.8602900000001, "r": 587.46674, "b": 409.63251, "coord_origin": "TOPLEFT"}}, {"id": 10, "text": "by the IBM International ", "bbox": {"l": 467.3399999999999, "t": 410.90067, "r": 566.34229, "b": 420.67285, "coord_origin": "TOPLEFT"}}, {"id": 11, "text": "Technical Support ", "bbox": {"l": 467.3399999999999, "t": 421.88068, "r": 543.20404, "b": 431.65289, "coord_origin": "TOPLEFT"}}, {"id": 12, "text": "Organization. Experts from ", "bbox": {"l": 467.3399999999999, "t": 432.8606899999999, "r": 577.76697, "b": 442.63287, "coord_origin": "TOPLEFT"}}, {"id": 13, "text": "IBM, Customers and Partners ", "bbox": {"l": 467.3399999999999, "t": 443.90106, "r": 587.40948, "b": 453.67328, "coord_origin": "TOPLEFT"}}, {"id": 14, "text": "from around the world create ", "bbox": {"l": 467.3399999999999, "t": 454.88107, "r": 587.52051, "b": 464.65326, "coord_origin": "TOPLEFT"}}, {"id": 15, "text": "timely technical information ", "bbox": {"l": 467.3399999999999, "t": 465.86108, "r": 582.67505, "b": 475.6333, "coord_origin": "TOPLEFT"}}, {"id": 16, "text": "based on realistic scenarios. ", "bbox": {"l": 467.3399999999999, "t": 476.90146, "r": 585.46722, "b": 486.67365, "coord_origin": "TOPLEFT"}}, {"id": 17, "text": "Specific recommendations ", "bbox": {"l": 467.3399999999999, "t": 487.88147, "r": 577.70874, "b": 497.65369, "coord_origin": "TOPLEFT"}}, {"id": 18, "text": "are provided to help you ", "bbox": {"l": 467.3399999999999, "t": 498.86148, "r": 568.03546, "b": 508.63367, "coord_origin": "TOPLEFT"}}, {"id": 19, "text": "implement IT solutions more ", "bbox": {"l": 467.3399999999999, "t": 509.90186, "r": 585.44525, "b": 519.67407, "coord_origin": "TOPLEFT"}}, {"id": 20, "text": "effectively in your ", "bbox": {"l": 467.3399999999999, "t": 520.8818699999999, "r": 541.4967, "b": 530.65405, "coord_origin": "TOPLEFT"}}, {"id": 21, "text": "environment.", "bbox": {"l": 467.3399999999999, "t": 531.8618799999999, "r": 520.64893, "b": 541.63406, "coord_origin": "TOPLEFT"}}, {"id": 22, "text": "For more information:", "bbox": {"l": 467.3399999999999, "t": 578.83191, "r": 570.948, "b": 589.5119, "coord_origin": "TOPLEFT"}}, {"id": 23, "text": "ibm.com", "bbox": {"l": 467.3399999999999, "t": 590.83191, "r": 508.59961, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 24, "text": "/redbooks", "bbox": {"l": 508.56000000000006, "t": 590.83191, "r": 552.74518, "b": 601.5119, "coord_origin": "TOPLEFT"}}, {"id": 25, "text": "Redpaper", "bbox": {"l": 474.60001, "t": 164.05658000000005, "r": 580.88989, "b": 188.94097999999997, "coord_origin": "TOPLEFT"}}, {"id": 26, "text": "\u2122", "bbox": {"l": 582.53992, "t": 172.32714999999996, "r": 592.13989, "b": 181.20714999999996, "coord_origin": "TOPLEFT"}}, {"id": 27, "text": "Row and Column Access Control ", "bbox": {"l": 27.0, "t": 73.63799999999992, "r": 447.36002, "b": 103.00800000000004, "coord_origin": "TOPLEFT"}}, {"id": 28, "text": "Support in IBM DB2 for i", "bbox": {"l": 27.0, "t": 113.76000999999997, "r": 314.43002, "b": 140.46002, "coord_origin": "TOPLEFT"}}, {"id": 29, "text": "Implement roles and ", "bbox": {"l": 26.700001, "t": 242.17200000000003, "r": 127.4436, "b": 252.85199, "coord_origin": "TOPLEFT"}}, {"id": 30, "text": "separation of duties", "bbox": {"l": 26.700001, "t": 256.15198, "r": 121.6608, "b": 266.83196999999996, "coord_origin": "TOPLEFT"}}, {"id": 31, "text": "Leverage row ", "bbox": {"l": 26.700001, "t": 284.17197, "r": 93.970795, "b": 294.85196, "coord_origin": "TOPLEFT"}}, {"id": 32, "text": "permissions on the ", "bbox": {"l": 26.700001, "t": 298.15198000000004, "r": 120.28319999999998, "b": 308.83197, "coord_origin": "TOPLEFT"}}, {"id": 33, "text": "database", "bbox": {"l": 26.700001, "t": 312.19199000000003, "r": 70.413605, "b": 322.87198, "coord_origin": "TOPLEFT"}}, {"id": 34, "text": "Protect columns by ", "bbox": {"l": 26.700001, "t": 340.15198000000004, "r": 121.44960000000002, "b": 350.83197, "coord_origin": "TOPLEFT"}}, {"id": 35, "text": "defining column ", "bbox": {"l": 26.700001, "t": 354.19199000000003, "r": 106.5696, "b": 364.87198, "coord_origin": "TOPLEFT"}}, {"id": 36, "text": "masks", "bbox": {"l": 26.700001, "t": 368.1720000000001, "r": 58.194, "b": 378.85199, "coord_origin": "TOPLEFT"}}, {"id": 37, "text": "This IBM Redpaper publication provides information about the IBM i 7.2 ", "bbox": {"l": 152.94, "t": 242.72857999999997, "r": 413.99057, "b": 251.59295999999995, "coord_origin": "TOPLEFT"}}, {"id": 38, "text": "feature of IBM DB2 for i Row and Column Access Control (RCAC). 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This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 17, "cluster": {"id": 0, "label": "text", "bbox": {"l": 152.94002, "t": 331.70728, "r": 414.17383, "b": 388.57095, "coord_origin": "TOPLEFT"}, "confidence": 0.9799742698669434, "cells": [{"id": 44, "text": "This paper is intended for database engineers, data-centric application ", "bbox": {"l": 152.94002, "t": 331.70728, "r": 414.17383, "b": 340.57169, "coord_origin": "TOPLEFT"}}, {"id": 45, "text": "developers, and security officers who want to design and implement ", "bbox": {"l": 152.94002, "t": 343.70709, "r": 407.61029, "b": 352.5715, "coord_origin": "TOPLEFT"}}, {"id": 46, "text": "RCAC as a part of their data control and governance policy. A solid ", "bbox": {"l": 152.94002, "t": 355.70691, "r": 399.29565, "b": 364.57132, "coord_origin": "TOPLEFT"}}, {"id": 47, "text": "background in IBM i object level security, DB2 for i relational database ", "bbox": {"l": 152.94002, "t": 367.70673, "r": 414.0603, "b": 376.57114, "coord_origin": "TOPLEFT"}}, {"id": 48, "text": "concepts, and SQL is assumed.", "bbox": {"l": 152.94002, "t": 379.70654, "r": 268.86945, "b": 388.57095, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "text", "id": 16, "page_no": 17, "cluster": {"id": 16, "label": "text", "bbox": {"l": 287.22, "t": 28.54803000000004, "r": 414.24481, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"id": 49, "text": "Back cover", "bbox": {"l": 287.22, "t": 28.54803000000004, "r": 414.24481, "b": 50.748050000000035, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Back cover"}], "headers": [{"label": "page_footer", "id": 10, "page_no": 17, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}, "confidence": 0.7700252532958984, "cells": [{"id": 1, "text": "REDP-5110-00", "bbox": {"l": 171.0, "t": 631.338, "r": 231.88769999999997, "b": 639.66301, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "REDP-5110-00"}]}}]
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt b/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt
index cab5f63b..216d68f8 100644
--- a/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt
+++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.itxt
@@ -7,4 +7,5 @@ item-0 at level 0: unspecified: group _root_
     item-6 at level 2: table with [5x3]
   item-7 at level 1: section: group sheet: Sheet3
     item-8 at level 2: table with [7x3]
-    item-9 at level 2: table with [7x3]
\ No newline at end of file
+    item-9 at level 2: table with [7x3]
+    item-10 at level 2: picture
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.json b/tests/data/groundtruth/docling_v2/test-01.xlsx.json
index 9a9e0d52..b6642298 100644
--- a/tests/data/groundtruth/docling_v2/test-01.xlsx.json
+++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.json
@@ -4,7 +4,7 @@
   "name": "test-01",
   "origin": {
     "mimetype": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-    "binary_hash": 9744611217659152490,
+    "binary_hash": 13665052226482254103,
     "filename": "test-01.xlsx"
   },
   "furniture": {
@@ -73,6 +73,9 @@
         },
         {
           "$ref": "#/tables/5"
+        },
+        {
+          "$ref": "#/pictures/0"
         }
       ],
       "name": "sheet: Sheet3",
@@ -80,7 +83,30 @@
     }
   ],
   "texts": [],
-  "pictures": [],
+  "pictures": [
+    {
+      "self_ref": "#/pictures/0",
+      "parent": {
+        "$ref": "#/groups/2"
+      },
+      "children": [],
+      "label": "picture",
+      "prov": [],
+      "captions": [],
+      "references": [],
+      "footnotes": [],
+      "image": {
+        "mimetype": "image/png",
+        "dpi": 72,
+        "size": {
+          "width": 421.0,
+          "height": 430.0
+        },
+        "uri": 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"
+      },
+      "annotations": []
+    }
+  ],
   "tables": [
     {
       "self_ref": "#/tables/0",
diff --git a/tests/data/groundtruth/docling_v2/test-01.xlsx.md b/tests/data/groundtruth/docling_v2/test-01.xlsx.md
index 4a059c60..cfd30c5c 100644
--- a/tests/data/groundtruth/docling_v2/test-01.xlsx.md
+++ b/tests/data/groundtruth/docling_v2/test-01.xlsx.md
@@ -48,4 +48,6 @@
 | 3           | 4            | 5            |
 | 3           | 6            | 7            |
 | 8           | 9            | 9            |
-| 10          | 9            | 9            |
\ No newline at end of file
+| 10          | 9            | 9            |
+
+<!-- image -->
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.itxt b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.itxt
new file mode 100644
index 00000000..fe317728
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.itxt
@@ -0,0 +1,52 @@
+item-0 at level 0: unspecified: group _root_
+  item-1 at level 1: title: Test Document
+    item-2 at level 2: paragraph: 
+    item-3 at level 2: section_header: Section 1
+  item-4 at level 1: paragraph: 
+  item-5 at level 1: paragraph: Paragraph 1.1
+  item-6 at level 1: paragraph: 
+  item-7 at level 1: paragraph: Paragraph 1.2
+  item-8 at level 1: paragraph: 
+  item-9 at level 1: section: group header-0
+    item-10 at level 2: section: group header-1
+      item-11 at level 3: section_header: Section 1.1
+        item-12 at level 4: paragraph: 
+        item-13 at level 4: paragraph: Paragraph 1.1.1
+        item-14 at level 4: paragraph: 
+        item-15 at level 4: paragraph: Paragraph 1.1.2
+        item-16 at level 4: paragraph: 
+      item-17 at level 3: section_header: Section 1.2
+        item-18 at level 4: paragraph: 
+        item-19 at level 4: paragraph: Paragraph 1.1.1
+        item-20 at level 4: paragraph: 
+        item-21 at level 4: paragraph: Paragraph 1.1.2
+        item-22 at level 4: paragraph: 
+        item-23 at level 4: section_header: Section 1.2.3
+          item-24 at level 5: paragraph: 
+          item-25 at level 5: paragraph: Paragraph 1.2.3.1
+          item-26 at level 5: paragraph: 
+          item-27 at level 5: paragraph: Paragraph 1.2.3.1
+          item-28 at level 5: paragraph: 
+          item-29 at level 5: paragraph: 
+    item-30 at level 2: section_header: Section 2
+  item-31 at level 1: paragraph: 
+  item-32 at level 1: paragraph: Paragraph 2.1
+  item-33 at level 1: paragraph: 
+  item-34 at level 1: paragraph: Paragraph 2.2
+  item-35 at level 1: paragraph: 
+  item-36 at level 1: section: group header-0
+    item-37 at level 2: section: group header-1
+      item-38 at level 3: section: group header-2
+        item-39 at level 4: section_header: Section 2.1.1
+          item-40 at level 5: paragraph: 
+          item-41 at level 5: paragraph: Paragraph 2.1.1.1
+          item-42 at level 5: paragraph: 
+          item-43 at level 5: paragraph: Paragraph 2.1.1.1
+          item-44 at level 5: paragraph: 
+      item-45 at level 3: section_header: Section 2.1
+        item-46 at level 4: paragraph: 
+        item-47 at level 4: paragraph: Paragraph 2.1.1
+        item-48 at level 4: paragraph: 
+        item-49 at level 4: paragraph: Paragraph 2.1.2
+        item-50 at level 4: paragraph: 
+        item-51 at level 4: paragraph: 
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json
new file mode 100644
index 00000000..38a25d33
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json
@@ -0,0 +1,753 @@
+{
+  "schema_name": "DoclingDocument",
+  "version": "1.0.0",
+  "name": "unit_test_headers_numbered",
+  "origin": {
+    "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    "binary_hash": 7684538628968220703,
+    "filename": "unit_test_headers_numbered.docx"
+  },
+  "furniture": {
+    "self_ref": "#/furniture",
+    "children": [],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "body": {
+    "self_ref": "#/body",
+    "children": [
+      {
+        "$ref": "#/texts/0"
+      },
+      {
+        "$ref": "#/texts/3"
+      },
+      {
+        "$ref": "#/texts/4"
+      },
+      {
+        "$ref": "#/texts/5"
+      },
+      {
+        "$ref": "#/texts/6"
+      },
+      {
+        "$ref": "#/texts/7"
+      },
+      {
+        "$ref": "#/groups/0"
+      },
+      {
+        "$ref": "#/texts/28"
+      },
+      {
+        "$ref": "#/texts/29"
+      },
+      {
+        "$ref": "#/texts/30"
+      },
+      {
+        "$ref": "#/texts/31"
+      },
+      {
+        "$ref": "#/texts/32"
+      },
+      {
+        "$ref": "#/groups/2"
+      }
+    ],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "groups": [
+    {
+      "self_ref": "#/groups/0",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [
+        {
+          "$ref": "#/groups/1"
+        },
+        {
+          "$ref": "#/texts/27"
+        }
+      ],
+      "name": "header-0",
+      "label": "section"
+    },
+    {
+      "self_ref": "#/groups/1",
+      "parent": {
+        "$ref": "#/groups/0"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/8"
+        },
+        {
+          "$ref": "#/texts/14"
+        }
+      ],
+      "name": "header-1",
+      "label": "section"
+    },
+    {
+      "self_ref": "#/groups/2",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [
+        {
+          "$ref": "#/groups/3"
+        }
+      ],
+      "name": "header-0",
+      "label": "section"
+    },
+    {
+      "self_ref": "#/groups/3",
+      "parent": {
+        "$ref": "#/groups/2"
+      },
+      "children": [
+        {
+          "$ref": "#/groups/4"
+        },
+        {
+          "$ref": "#/texts/39"
+        }
+      ],
+      "name": "header-1",
+      "label": "section"
+    },
+    {
+      "self_ref": "#/groups/4",
+      "parent": {
+        "$ref": "#/groups/3"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/33"
+        }
+      ],
+      "name": "header-2",
+      "label": "section"
+    }
+  ],
+  "texts": [
+    {
+      "self_ref": "#/texts/0",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/1"
+        },
+        {
+          "$ref": "#/texts/2"
+        }
+      ],
+      "label": "title",
+      "prov": [],
+      "orig": "Test Document",
+      "text": "Test Document"
+    },
+    {
+      "self_ref": "#/texts/1",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/2",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 1",
+      "text": "Section 1",
+      "level": 1
+    },
+    {
+      "self_ref": "#/texts/3",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/4",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.1",
+      "text": "Paragraph 1.1"
+    },
+    {
+      "self_ref": "#/texts/5",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/6",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.2",
+      "text": "Paragraph 1.2"
+    },
+    {
+      "self_ref": "#/texts/7",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/8",
+      "parent": {
+        "$ref": "#/groups/1"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/9"
+        },
+        {
+          "$ref": "#/texts/10"
+        },
+        {
+          "$ref": "#/texts/11"
+        },
+        {
+          "$ref": "#/texts/12"
+        },
+        {
+          "$ref": "#/texts/13"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 1.1",
+      "text": "Section 1.1",
+      "level": 2
+    },
+    {
+      "self_ref": "#/texts/9",
+      "parent": {
+        "$ref": "#/texts/8"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/10",
+      "parent": {
+        "$ref": "#/texts/8"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.1.1",
+      "text": "Paragraph 1.1.1"
+    },
+    {
+      "self_ref": "#/texts/11",
+      "parent": {
+        "$ref": "#/texts/8"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/12",
+      "parent": {
+        "$ref": "#/texts/8"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.1.2",
+      "text": "Paragraph 1.1.2"
+    },
+    {
+      "self_ref": "#/texts/13",
+      "parent": {
+        "$ref": "#/texts/8"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/14",
+      "parent": {
+        "$ref": "#/groups/1"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/15"
+        },
+        {
+          "$ref": "#/texts/16"
+        },
+        {
+          "$ref": "#/texts/17"
+        },
+        {
+          "$ref": "#/texts/18"
+        },
+        {
+          "$ref": "#/texts/19"
+        },
+        {
+          "$ref": "#/texts/20"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 1.2",
+      "text": "Section 1.2",
+      "level": 2
+    },
+    {
+      "self_ref": "#/texts/15",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/16",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.1.1",
+      "text": "Paragraph 1.1.1"
+    },
+    {
+      "self_ref": "#/texts/17",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/18",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.1.2",
+      "text": "Paragraph 1.1.2"
+    },
+    {
+      "self_ref": "#/texts/19",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/20",
+      "parent": {
+        "$ref": "#/texts/14"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/21"
+        },
+        {
+          "$ref": "#/texts/22"
+        },
+        {
+          "$ref": "#/texts/23"
+        },
+        {
+          "$ref": "#/texts/24"
+        },
+        {
+          "$ref": "#/texts/25"
+        },
+        {
+          "$ref": "#/texts/26"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 1.2.3",
+      "text": "Section 1.2.3",
+      "level": 3
+    },
+    {
+      "self_ref": "#/texts/21",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/22",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.2.3.1",
+      "text": "Paragraph 1.2.3.1"
+    },
+    {
+      "self_ref": "#/texts/23",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/24",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 1.2.3.1",
+      "text": "Paragraph 1.2.3.1"
+    },
+    {
+      "self_ref": "#/texts/25",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/26",
+      "parent": {
+        "$ref": "#/texts/20"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/27",
+      "parent": {
+        "$ref": "#/groups/0"
+      },
+      "children": [],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 2",
+      "text": "Section 2",
+      "level": 1
+    },
+    {
+      "self_ref": "#/texts/28",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/29",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.1",
+      "text": "Paragraph 2.1"
+    },
+    {
+      "self_ref": "#/texts/30",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/31",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.2",
+      "text": "Paragraph 2.2"
+    },
+    {
+      "self_ref": "#/texts/32",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/33",
+      "parent": {
+        "$ref": "#/groups/4"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/34"
+        },
+        {
+          "$ref": "#/texts/35"
+        },
+        {
+          "$ref": "#/texts/36"
+        },
+        {
+          "$ref": "#/texts/37"
+        },
+        {
+          "$ref": "#/texts/38"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 2.1.1",
+      "text": "Section 2.1.1",
+      "level": 3
+    },
+    {
+      "self_ref": "#/texts/34",
+      "parent": {
+        "$ref": "#/texts/33"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/35",
+      "parent": {
+        "$ref": "#/texts/33"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.1.1.1",
+      "text": "Paragraph 2.1.1.1"
+    },
+    {
+      "self_ref": "#/texts/36",
+      "parent": {
+        "$ref": "#/texts/33"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/37",
+      "parent": {
+        "$ref": "#/texts/33"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.1.1.1",
+      "text": "Paragraph 2.1.1.1"
+    },
+    {
+      "self_ref": "#/texts/38",
+      "parent": {
+        "$ref": "#/texts/33"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/39",
+      "parent": {
+        "$ref": "#/groups/3"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/40"
+        },
+        {
+          "$ref": "#/texts/41"
+        },
+        {
+          "$ref": "#/texts/42"
+        },
+        {
+          "$ref": "#/texts/43"
+        },
+        {
+          "$ref": "#/texts/44"
+        },
+        {
+          "$ref": "#/texts/45"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Section 2.1",
+      "text": "Section 2.1",
+      "level": 2
+    },
+    {
+      "self_ref": "#/texts/40",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/41",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.1.1",
+      "text": "Paragraph 2.1.1"
+    },
+    {
+      "self_ref": "#/texts/42",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/43",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "Paragraph 2.1.2",
+      "text": "Paragraph 2.1.2"
+    },
+    {
+      "self_ref": "#/texts/44",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/45",
+      "parent": {
+        "$ref": "#/texts/39"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    }
+  ],
+  "pictures": [],
+  "tables": [],
+  "key_value_items": [],
+  "pages": {}
+}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.md b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.md
new file mode 100644
index 00000000..d4c8accd
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.md
@@ -0,0 +1,43 @@
+# Test Document
+
+## Section 1
+
+Paragraph 1.1
+
+Paragraph 1.2
+
+### Section 1.1
+
+Paragraph 1.1.1
+
+Paragraph 1.1.2
+
+### Section 1.2
+
+Paragraph 1.1.1
+
+Paragraph 1.1.2
+
+#### Section 1.2.3
+
+Paragraph 1.2.3.1
+
+Paragraph 1.2.3.1
+
+## Section 2
+
+Paragraph 2.1
+
+Paragraph 2.2
+
+#### Section 2.1.1
+
+Paragraph 2.1.1.1
+
+Paragraph 2.1.1.1
+
+### Section 2.1
+
+Paragraph 2.1.1
+
+Paragraph 2.1.2
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/wiki.md.md b/tests/data/groundtruth/docling_v2/wiki.md.md
new file mode 100644
index 00000000..134e456e
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/wiki.md.md
@@ -0,0 +1,23 @@
+# IBM
+
+International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.
+
+It is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Average.
+
+IBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for 29 consecutive years from 1993 to 2021.
+
+IBM was founded in 1911 as the Computing-Tabulating-Recording Company (CTR), a holding company of manufacturers of record-keeping and measuring systems. It was renamed "International Business Machines" in 1924 and soon became the leading manufacturer of punch-card tabulating systems. During the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world's dominant computing platform, with the company producing 80 percent of computers in the U.S. and 70 percent of computers worldwide.[11]
+
+IBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.[12] The company later also found success in the portable space with the ThinkPad. Since the 1990s, IBM has concentrated on computer services, software, supercomputers, and scientific research; it sold its microcomputer division to Lenovo in 2005. IBM continues to develop mainframes, and its supercomputers have consistently ranked among the most powerful in the world in the 21st century.
+
+As one of the world's oldest and largest technology companies, IBM has been responsible for several technological innovations, including the automated teller machine (ATM), dynamic random-access memory (DRAM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the SQL programming language, and the UPC barcode. The company has made inroads in advanced computer chips, quantum computing, artificial intelligence, and data infrastructure.[13][14][15] IBM employees and alumni have won various recognitions for their scientific research and inventions, including six Nobel Prizes and six Turing Awards.[16]
+
+## 1910s–1950s
+
+IBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;[17] Alexander Dey invented the dial recorder (1888);[18] Herman Hollerith patented the Electric Tabulating Machine (1889);[19] and Willard Bundy invented a time clock to record workers' arrival and departure times on a paper tape (1889).[20] On June 16, 1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company (CTR) based in Endicott, New York.[1][21] The five companies had 1,300 employees and offices and plants in Endicott and Binghamton, New York; Dayton, Ohio; Detroit, Michigan; Washington, D.C.; and Toronto, Canada.[22]
+
+Collectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR.[23] Watson joined CTR as general manager and then, 11 months later, was made President when antitrust cases relating to his time at NCR were resolved.[24] Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies.[23]: 105  He implemented sales conventions, "generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker".[25][26] His favorite slogan, "THINK", became a mantra for each company's employees.[25] During Watson's first four years, revenues reached $9 million ($158 million today) and the company's operations expanded to Europe, South America, Asia and Australia.[25] Watson never liked the clumsy hyphenated name "Computing-Tabulating-Recording Company" and chose to replace it with the more expansive title "International Business Machines" which had previously been used as the name of CTR's Canadian Division;[27] the name was changed on February 14, 1924.[28] By 1933, most of the subsidiaries had been merged into one company, IBM.
+
+## 1960s–1980s
+
+In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.
diff --git a/tests/data/groundtruth/docling_v2/wiki_duck.html.md b/tests/data/groundtruth/docling_v2/wiki_duck.html.md
index 856e97a7..df4554fc 100644
--- a/tests/data/groundtruth/docling_v2/wiki_duck.html.md
+++ b/tests/data/groundtruth/docling_v2/wiki_duck.html.md
@@ -389,22 +389,22 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a
 4. ^ Visca, Curt; Visca, Kelley (2003). How to Draw Cartoon Birds. The Rosen Publishing Group. ISBN 9780823961566.
 5. ^ a b c d Carboneras 1992, p. 536.
 6. ^ Livezey 1986, pp. 737–738.
-7. ^ Madsen, McHugh & de Kloet 1988, p. 452.
-8. ^ Donne-Goussé, Laudet & Hänni 2002, pp. 353–354.
+7. ^ Madsen, McHugh &amp; de Kloet 1988, p. 452.
+8. ^ Donne-Goussé, Laudet &amp; Hänni 2002, pp. 353–354.
 9. ^ a b c d e f Carboneras 1992, p. 540.
-10. ^ Elphick, Dunning & Sibley 2001, p. 191.
+10. ^ Elphick, Dunning &amp; Sibley 2001, p. 191.
 11. ^ Kear 2005, p. 448.
 12. ^ Kear 2005, p. 622–623.
 13. ^ Kear 2005, p. 686.
-14. ^ Elphick, Dunning & Sibley 2001, p. 193.
+14. ^ Elphick, Dunning &amp; Sibley 2001, p. 193.
 15. ^ a b c d e f g Carboneras 1992, p. 537.
 16. ^ American Ornithologists' Union 1998, p. xix.
 17. ^ American Ornithologists' Union 1998.
 18. ^ Carboneras 1992, p. 538.
-19. ^ Christidis & Boles 2008, p. 62.
+19. ^ Christidis &amp; Boles 2008, p. 62.
 20. ^ Shirihai 2008, pp. 239, 245.
-21. ^ a b Pratt, Bruner & Berrett 1987, pp. 98–107.
-22. ^ Fitter, Fitter & Hosking 2000, pp. 52–3.
+21. ^ a b Pratt, Bruner &amp; Berrett 1987, pp. 98–107.
+22. ^ Fitter, Fitter &amp; Hosking 2000, pp. 52–3.
 23. ^ "Pacific Black Duck". www.wiresnr.org. Retrieved 2018-04-27.
 24. ^ Ogden, Evans. "Dabbling Ducks". CWE. Retrieved 2006-11-02.
 25. ^ Karl Mathiesen (16 March 2015). "Don't feed the ducks bread, say conservationists". The Guardian. Retrieved 13 November 2016.
@@ -412,7 +412,7 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a
 27. ^ Smith, Cyndi M.; Cooke, Fred; Robertson, Gregory J.; Goudie, R. Ian; Boyd, W. Sean (2000). "Long-Term Pair Bonds in Harlequin Ducks". The Condor. 102 (1): 201–205. doi:10.1093/condor/102.1.201. hdl:10315/13797.
 28. ^ "If You Find An Orphaned Duckling - Wildlife Rehabber". wildliferehabber.com. Archived from the original on 2018-09-23. Retrieved 2018-12-22.
 29. ^ Carver, Heather (2011). The Duck Bible. Lulu.com. ISBN 9780557901562.[self-published source]
-30. ^ Titlow, Budd (2013-09-03). Bird Brains: Inside the Strange Minds of Our Fine Feathered Friends. Rowman & Littlefield. ISBN 9780762797707.
+30. ^ Titlow, Budd (2013-09-03). Bird Brains: Inside the Strange Minds of Our Fine Feathered Friends. Rowman &amp; Littlefield. ISBN 9780762797707.
 31. ^ Amos, Jonathan (2003-09-08). "Sound science is quackers". BBC News. Retrieved 2006-11-02.
 32. ^ "Mythbusters Episode 8". 12 December 2003.
 33. ^ Erlandson 1994, p. 171.
@@ -446,10 +446,10 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a
 - Christidis, Les; Boles, Walter E., eds. (2008). Systematics and Taxonomy of Australian Birds. Collingwood, VIC: Csiro Publishing. ISBN 978-0-643-06511-6.
 - Donne-Goussé, Carole; Laudet, Vincent; Hänni, Catherine (July 2002). "A molecular phylogeny of Anseriformes based on mitochondrial DNA analysis". Molecular Phylogenetics and Evolution. 23 (3): 339–356. Bibcode:2002MolPE..23..339D. doi:10.1016/S1055-7903(02)00019-2. PMID 12099792.
 - Elphick, Chris; Dunning, John B. Jr.; Sibley, David, eds. (2001). The Sibley Guide to Bird Life and Behaviour. London: Christopher Helm. ISBN 978-0-7136-6250-4.
-- Erlandson, Jon M. (1994). Early Hunter-Gatherers of the California Coast. New York, NY: Springer Science & Business Media. ISBN 978-1-4419-3231-0.
+- Erlandson, Jon M. (1994). Early Hunter-Gatherers of the California Coast. New York, NY: Springer Science &amp; Business Media. ISBN 978-1-4419-3231-0.
 - Fieldhouse, Paul (2002). Food, Feasts, and Faith: An Encyclopedia of Food Culture in World Religions. Vol. I: A–K. Santa Barbara: ABC-CLIO. ISBN 978-1-61069-412-4.
 - Fitter, Julian; Fitter, Daniel; Hosking, David (2000). Wildlife of the Galápagos. Princeton, NJ: Princeton University Press. ISBN 978-0-691-10295-5.
-- Higman, B. W. (2012). How Food Made History. Chichester, UK: John Wiley & Sons. ISBN 978-1-4051-8947-7.
+- Higman, B. W. (2012). How Food Made History. Chichester, UK: John Wiley &amp; Sons. ISBN 978-1-4051-8947-7.
 - Hume, Julian H. (2012). Extinct Birds. London: Christopher Helm. ISBN 978-1-4729-3744-5.
 - Jeffries, Richard (2008). Holocene Hunter-Gatherers of the Lower Ohio River Valley. Tuscaloosa: University of Alabama Press. ISBN 978-0-8173-1658-7.
 - Kear, Janet, ed. (2005). Ducks, Geese and Swans: Species Accounts (Cairina to Mergus). Bird Families of the World. Oxford: Oxford University Press. ISBN 978-0-19-861009-0.
@@ -457,7 +457,7 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a
 - Madsen, Cort S.; McHugh, Kevin P.; de Kloet, Siwo R. (July 1988). "A partial classification of waterfowl (Anatidae) based on single-copy DNA" (PDF). The Auk. 105 (3): 452–459. doi:10.1093/auk/105.3.452. Archived (PDF) from the original on 2022-10-09.
 - Maisels, Charles Keith (1999). Early Civilizations of the Old World. London: Routledge. ISBN 978-0-415-10975-8.
 - Pratt, H. Douglas; Bruner, Phillip L.; Berrett, Delwyn G. (1987). A Field Guide to the Birds of Hawaii and the Tropical Pacific. Princeton, NJ: Princeton University Press. ISBN 0-691-02399-9.
-- Rau, Charles (1876). Early Man in Europe. New York: Harper & Brothers. LCCN 05040168.
+- Rau, Charles (1876). Early Man in Europe. New York: Harper &amp; Brothers. LCCN 05040168.
 - Shirihai, Hadoram (2008). A Complete Guide to Antarctic Wildlife. Princeton, NJ, US: Princeton University Press. ISBN 978-0-691-13666-0.
 - Sued-Badillo, Jalil (2003). Autochthonous Societies. General History of the Caribbean. Paris: UNESCO. ISBN 978-92-3-103832-7.
 - Thorpe, I. J. (1996). The Origins of Agriculture in Europe. New York: Routledge. ISBN 978-0-415-08009-5.
diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.html b/tests/data/groundtruth/docling_v2/word_tables.docx.html
new file mode 100644
index 00000000..00a790c0
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/word_tables.docx.html
@@ -0,0 +1,89 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <link rel="icon" type="image/png"
+    href="https://ds4sd.github.io/docling/assets/logo.png"/>
+    <meta charset="UTF-8">
+    <title>
+    Powered by Docling
+    </title>
+    <style>
+    html {
+    background-color: LightGray;
+    }
+    body {
+    margin: 0 auto;
+    width:800px;
+    padding: 30px;
+    background-color: White;
+    font-family: Arial, sans-serif;
+    box-shadow: 10px 10px 10px grey;
+    }
+    figure{
+    display: block;
+    width: 100%;
+    margin: 0px;
+    margin-top: 10px;
+    margin-bottom: 10px;
+    }
+    img {
+    display: block;
+    margin: auto;
+    margin-top: 10px;
+    margin-bottom: 10px;
+    max-width: 640px;
+    max-height: 640px;
+    }
+    table {
+    min-width:500px;
+    background-color: White;
+    border-collapse: collapse;
+    cell-padding: 5px;
+    margin: auto;
+    margin-top: 10px;
+    margin-bottom: 10px;
+    }
+    th, td {
+    border: 1px solid black;
+    padding: 8px;
+    }
+    th {
+    font-weight: bold;
+    }
+    table tr:nth-child(even) td{
+    background-color: LightGray;
+    }
+    math annotation {
+    display: none;
+    }
+    .formula-not-decoded {
+    background: repeating-linear-gradient(
+    45deg, /* Angle of the stripes */
+    LightGray, /* First color */
+    LightGray 10px, /* Length of the first color */
+    White 10px, /* Second color */
+    White 20px /* Length of the second color */
+    );
+    margin: 0;
+    text-align: center;
+    }
+    </style>
+    </head>
+<h2>Test with tables</h2>
+<p>A uniform table</p>
+<table><tbody><tr><td>Header 0.0</td><td>Header 0.1</td><td>Header 0.2</td></tr><tr><td>Cell 1.0</td><td>Cell 1.1</td><td>Cell 1.2</td></tr><tr><td>Cell 2.0</td><td>Cell 2.1</td><td>Cell 2.2</td></tr></tbody></table>
+<p></p>
+<p>A non-uniform table with horizontal spans</p>
+<table><tbody><tr><td>Header 0.0</td><td>Header 0.1</td><td>Header 0.2</td></tr><tr><td>Cell 1.0</td><td colspan="2">Merged Cell 1.1 1.2</td></tr><tr><td>Cell 2.0</td><td colspan="2">Merged Cell 2.1 2.2</td></tr></tbody></table>
+<p></p>
+<p>A non-uniform table with horizontal spans in inner columns</p>
+<table><tbody><tr><td>Header 0.0</td><td>Header 0.1</td><td>Header 0.2</td><td>Header 0.3</td></tr><tr><td>Cell 1.0</td><td colspan="2">Merged Cell 1.1 1.2</td><td>Cell 1.3</td></tr><tr><td>Cell 2.0</td><td colspan="2">Merged Cell 2.1 2.2</td><td>Cell 2.3</td></tr></tbody></table>
+<p></p>
+<p>A non-uniform table with vertical spans</p>
+<table><tbody><tr><td>Header 0.0</td><td>Header 0.1</td><td>Header 0.2</td></tr><tr><td>Cell 1.0</td><td rowspan="2">Merged Cell 1.1 2.1</td><td>Cell 1.2</td></tr><tr><td>Cell 2.0</td><td>Cell 2.2</td></tr><tr><td>Cell 3.0</td><td rowspan="2">Merged Cell 3.1 4.1</td><td>Cell 3.2</td></tr><tr><td>Cell 4.0</td><td>Cell 4.2</td></tr></tbody></table>
+<p></p>
+<p>A non-uniform table with all kinds of spans and empty cells</p>
+<table><tbody><tr><td>Header 0.0</td><td>Header 0.1</td><td>Header 0.2</td><td></td><td></td></tr><tr><td>Cell 1.0</td><td rowspan="2">Merged Cell 1.1 2.1</td><td>Cell 1.2</td><td></td><td></td></tr><tr><td>Cell 2.0</td><td>Cell 2.2</td><td></td><td></td></tr><tr><td>Cell 3.0</td><td rowspan="2">Merged Cell 3.1 4.1</td><td>Cell 3.2</td><td rowspan="3"></td><td></td></tr><tr><td>Cell 4.0</td><td>Cell 4.2</td><td rowspan="2">Merged Cell 4.4 5.4</td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="5"></td></tr><tr><td></td><td></td><td></td><td></td><td>Cell 8.4</td></tr></tbody></table>
+<p></p>
+<p></p>
+</html>
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.itxt b/tests/data/groundtruth/docling_v2/word_tables.docx.itxt
new file mode 100644
index 00000000..dd42eb0a
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/word_tables.docx.itxt
@@ -0,0 +1,19 @@
+item-0 at level 0: unspecified: group _root_
+  item-1 at level 1: section: group header-0
+    item-2 at level 2: section_header: Test with tables
+      item-3 at level 3: paragraph: A uniform table
+      item-4 at level 3: table with [3x3]
+      item-5 at level 3: paragraph: 
+      item-6 at level 3: paragraph: A non-uniform table with horizontal spans
+      item-7 at level 3: table with [3x3]
+      item-8 at level 3: paragraph: 
+      item-9 at level 3: paragraph: A non-uniform table with horizontal spans in inner columns
+      item-10 at level 3: table with [3x4]
+      item-11 at level 3: paragraph: 
+      item-12 at level 3: paragraph: A non-uniform table with vertical spans
+      item-13 at level 3: table with [5x3]
+      item-14 at level 3: paragraph: 
+      item-15 at level 3: paragraph: A non-uniform table with all kinds of spans and empty cells
+      item-16 at level 3: table with [9x5]
+      item-17 at level 3: paragraph: 
+      item-18 at level 3: paragraph: 
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.json b/tests/data/groundtruth/docling_v2/word_tables.docx.json
new file mode 100644
index 00000000..957a83c8
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/word_tables.docx.json
@@ -0,0 +1,2356 @@
+{
+  "schema_name": "DoclingDocument",
+  "version": "1.0.0",
+  "name": "word_tables",
+  "origin": {
+    "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    "binary_hash": 8379738677198259833,
+    "filename": "word_tables.docx"
+  },
+  "furniture": {
+    "self_ref": "#/furniture",
+    "children": [],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "body": {
+    "self_ref": "#/body",
+    "children": [
+      {
+        "$ref": "#/groups/0"
+      }
+    ],
+    "name": "_root_",
+    "label": "unspecified"
+  },
+  "groups": [
+    {
+      "self_ref": "#/groups/0",
+      "parent": {
+        "$ref": "#/body"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/0"
+        }
+      ],
+      "name": "header-0",
+      "label": "section"
+    }
+  ],
+  "texts": [
+    {
+      "self_ref": "#/texts/0",
+      "parent": {
+        "$ref": "#/groups/0"
+      },
+      "children": [
+        {
+          "$ref": "#/texts/1"
+        },
+        {
+          "$ref": "#/tables/0"
+        },
+        {
+          "$ref": "#/texts/2"
+        },
+        {
+          "$ref": "#/texts/3"
+        },
+        {
+          "$ref": "#/tables/1"
+        },
+        {
+          "$ref": "#/texts/4"
+        },
+        {
+          "$ref": "#/texts/5"
+        },
+        {
+          "$ref": "#/tables/2"
+        },
+        {
+          "$ref": "#/texts/6"
+        },
+        {
+          "$ref": "#/texts/7"
+        },
+        {
+          "$ref": "#/tables/3"
+        },
+        {
+          "$ref": "#/texts/8"
+        },
+        {
+          "$ref": "#/texts/9"
+        },
+        {
+          "$ref": "#/tables/4"
+        },
+        {
+          "$ref": "#/texts/10"
+        },
+        {
+          "$ref": "#/texts/11"
+        }
+      ],
+      "label": "section_header",
+      "prov": [],
+      "orig": "Test with tables",
+      "text": "Test with tables",
+      "level": 1
+    },
+    {
+      "self_ref": "#/texts/1",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "A uniform table",
+      "text": "A uniform table"
+    },
+    {
+      "self_ref": "#/texts/2",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/3",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "A non-uniform table with horizontal spans",
+      "text": "A non-uniform table with horizontal spans"
+    },
+    {
+      "self_ref": "#/texts/4",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/5",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "A non-uniform table with horizontal spans in inner columns",
+      "text": "A non-uniform table with horizontal spans in inner columns"
+    },
+    {
+      "self_ref": "#/texts/6",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/7",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "A non-uniform table with vertical spans",
+      "text": "A non-uniform table with vertical spans"
+    },
+    {
+      "self_ref": "#/texts/8",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/9",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "A non-uniform table with all kinds of spans and empty cells",
+      "text": "A non-uniform table with all kinds of spans and empty cells"
+    },
+    {
+      "self_ref": "#/texts/10",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    },
+    {
+      "self_ref": "#/texts/11",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "paragraph",
+      "prov": [],
+      "orig": "",
+      "text": ""
+    }
+  ],
+  "pictures": [],
+  "tables": [
+    {
+      "self_ref": "#/tables/0",
+      "parent": {
+        "$ref": "#/texts/0"
+      },
+      "children": [],
+      "label": "table",
+      "prov": [],
+      "captions": [],
+      "references": [],
+      "footnotes": [],
+      "data": {
+        "table_cells": [
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 0,
+            "end_row_offset_idx": 1,
+            "start_col_offset_idx": 0,
+            "end_col_offset_idx": 1,
+            "text": "Header 0.0",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 0,
+            "end_row_offset_idx": 1,
+            "start_col_offset_idx": 1,
+            "end_col_offset_idx": 2,
+            "text": "Header 0.1",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 0,
+            "end_row_offset_idx": 1,
+            "start_col_offset_idx": 2,
+            "end_col_offset_idx": 3,
+            "text": "Header 0.2",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 1,
+            "end_row_offset_idx": 2,
+            "start_col_offset_idx": 0,
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+            "text": "",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 8,
+            "end_row_offset_idx": 9,
+            "start_col_offset_idx": 2,
+            "end_col_offset_idx": 3,
+            "text": "",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 8,
+            "end_row_offset_idx": 9,
+            "start_col_offset_idx": 3,
+            "end_col_offset_idx": 4,
+            "text": "",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          },
+          {
+            "row_span": 1,
+            "col_span": 1,
+            "start_row_offset_idx": 8,
+            "end_row_offset_idx": 9,
+            "start_col_offset_idx": 4,
+            "end_col_offset_idx": 5,
+            "text": "Cell 8.4",
+            "column_header": false,
+            "row_header": false,
+            "row_section": false
+          }
+        ],
+        "num_rows": 9,
+        "num_cols": 5,
+        "grid": [
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Header 0.0",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Header 0.1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Header 0.2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 0,
+              "end_row_offset_idx": 1,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Cell 1.0",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Merged Cell 1.1 2.1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Cell 1.2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 2,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Cell 2.0",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 1,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Merged Cell 1.1 2.1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Cell 2.2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 2,
+              "end_row_offset_idx": 3,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Cell 3.0",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 5,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Merged Cell 3.1 4.1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Cell 3.2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 3,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 4,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 4,
+              "end_row_offset_idx": 5,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "Cell 4.0",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 5,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "Merged Cell 3.1 4.1",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 4,
+              "end_row_offset_idx": 5,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "Cell 4.2",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 3,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 4,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "Merged Cell 4.4 5.4",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 5,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 5,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 5,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 3,
+              "col_span": 1,
+              "start_row_offset_idx": 3,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 2,
+              "col_span": 1,
+              "start_row_offset_idx": 4,
+              "end_row_offset_idx": 6,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "Merged Cell 4.4 5.4",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 6,
+              "end_row_offset_idx": 7,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 6,
+              "end_row_offset_idx": 7,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 6,
+              "end_row_offset_idx": 7,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 6,
+              "end_row_offset_idx": 7,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 6,
+              "end_row_offset_idx": 7,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 5,
+              "start_row_offset_idx": 7,
+              "end_row_offset_idx": 8,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 5,
+              "start_row_offset_idx": 7,
+              "end_row_offset_idx": 8,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 5,
+              "start_row_offset_idx": 7,
+              "end_row_offset_idx": 8,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 5,
+              "start_row_offset_idx": 7,
+              "end_row_offset_idx": 8,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 5,
+              "start_row_offset_idx": 7,
+              "end_row_offset_idx": 8,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 5,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ],
+          [
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 8,
+              "end_row_offset_idx": 9,
+              "start_col_offset_idx": 0,
+              "end_col_offset_idx": 1,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 8,
+              "end_row_offset_idx": 9,
+              "start_col_offset_idx": 1,
+              "end_col_offset_idx": 2,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 8,
+              "end_row_offset_idx": 9,
+              "start_col_offset_idx": 2,
+              "end_col_offset_idx": 3,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 8,
+              "end_row_offset_idx": 9,
+              "start_col_offset_idx": 3,
+              "end_col_offset_idx": 4,
+              "text": "",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            },
+            {
+              "row_span": 1,
+              "col_span": 1,
+              "start_row_offset_idx": 8,
+              "end_row_offset_idx": 9,
+              "start_col_offset_idx": 4,
+              "end_col_offset_idx": 5,
+              "text": "Cell 8.4",
+              "column_header": false,
+              "row_header": false,
+              "row_section": false
+            }
+          ]
+        ]
+      }
+    }
+  ],
+  "key_value_items": [],
+  "pages": {}
+}
\ No newline at end of file
diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.md b/tests/data/groundtruth/docling_v2/word_tables.docx.md
new file mode 100644
index 00000000..90123c3e
--- /dev/null
+++ b/tests/data/groundtruth/docling_v2/word_tables.docx.md
@@ -0,0 +1,44 @@
+## Test with tables
+
+A uniform table
+
+| Header 0.0   | Header 0.1   | Header 0.2   |
+|--------------|--------------|--------------|
+| Cell 1.0     | Cell 1.1     | Cell 1.2     |
+| Cell 2.0     | Cell 2.1     | Cell 2.2     |
+
+A non-uniform table with horizontal spans
+
+| Header 0.0   | Header 0.1          | Header 0.2          |
+|--------------|---------------------|---------------------|
+| Cell 1.0     | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 |
+| Cell 2.0     | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 |
+
+A non-uniform table with horizontal spans in inner columns
+
+| Header 0.0   | Header 0.1          | Header 0.2          | Header 0.3   |
+|--------------|---------------------|---------------------|--------------|
+| Cell 1.0     | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3     |
+| Cell 2.0     | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3     |
+
+A non-uniform table with vertical spans
+
+| Header 0.0   | Header 0.1          | Header 0.2   |
+|--------------|---------------------|--------------|
+| Cell 1.0     | Merged Cell 1.1 2.1 | Cell 1.2     |
+| Cell 2.0     | Merged Cell 1.1 2.1 | Cell 2.2     |
+| Cell 3.0     | Merged Cell 3.1 4.1 | Cell 3.2     |
+| Cell 4.0     | Merged Cell 3.1 4.1 | Cell 4.2     |
+
+A non-uniform table with all kinds of spans and empty cells
+
+| Header 0.0   | Header 0.1          | Header 0.2   |    |                     |
+|--------------|---------------------|--------------|----|---------------------|
+| Cell 1.0     | Merged Cell 1.1 2.1 | Cell 1.2     |    |                     |
+| Cell 2.0     | Merged Cell 1.1 2.1 | Cell 2.2     |    |                     |
+| Cell 3.0     | Merged Cell 3.1 4.1 | Cell 3.2     |    |                     |
+| Cell 4.0     | Merged Cell 3.1 4.1 | Cell 4.2     |    | Merged Cell 4.4 5.4 |
+|              |                     |              |    | Merged Cell 4.4 5.4 |
+|              |                     |              |    |                     |
+|              |                     |              |    |                     |
+|              |                     |              |    | Cell 8.4            |
\ No newline at end of file
diff --git a/tests/data/html/example_05.html b/tests/data/html/example_05.html
new file mode 100644
index 00000000..e218ff1a
--- /dev/null
+++ b/tests/data/html/example_05.html
@@ -0,0 +1,20 @@
+<h1>Omitted html and body tags</h1>
+<table>
+    <tr>
+        <th>Header 1</th>
+        <th colspan="2">Header 2 & 3 (colspan)</th>
+    </tr>
+    <tr>
+        <td rowspan="2">Row 1 & 2, Col 1 (rowspan)</td>
+        <td>Row 1, Col 2</td>
+        <td>Row 1, Col 3</td>
+    </tr>
+    <tr>
+        <td colspan="2">Row 2, Col 2 & 3 (colspan)</td>
+    </tr>
+    <tr>
+        <td>Row 3, Col 1</td>
+        <td>Row 3, Col 2</td>
+        <td>Row 3, Col 3</td>
+    </tr>
+</table>
diff --git a/tests/data/md/blocks.md b/tests/data/md/blocks.md
new file mode 100644
index 00000000..9980bb68
--- /dev/null
+++ b/tests/data/md/blocks.md
@@ -0,0 +1,43 @@
+Unordered list:
+
+- foo
+
+Empty unordered list:
+
+-
+
+Ordered list:
+
+1. bar
+
+Empty ordered list:
+
+1.
+
+Heading:
+
+# my heading
+
+Empty heading:
+
+#
+
+Indented code block:
+
+    print("Hi!")
+
+Empty indented code block:
+
+    
+
+Fenced code block:
+
+```python
+print("Hello world!")
+```
+
+Empty fenced code block:
+
+```
+
+```
diff --git a/tests/data/md/duck.md b/tests/data/md/duck.md
new file mode 100644
index 00000000..6fb56917
--- /dev/null
+++ b/tests/data/md/duck.md
@@ -0,0 +1,56 @@
+Summer activities
+
+# Swimming in the lake
+
+Duck
+
+
+Figure 1: This is a cute duckling
+
+## Let’s swim!
+
+To get started with swimming, first lay down in a water and try not to drown:
+
+- You can relax and look around
+- Paddle about
+- Enjoy summer warmth
+
+Also, don’t forget:
+
+- Wear sunglasses
+- Don’t forget to drink water
+- Use sun cream
+
+Hmm, what else…
+
+## Let’s eat
+
+After we had a good day of swimming in the lake,
+it’s important to eat
+something nice
+
+I like to eat leaves
+
+
+Here are some interesting things a respectful duck could eat:
+
+|         | Food                             |   Calories per portion |
+|---------|----------------------------------|------------------------|
+| Leaves  | Ash, Elm, Maple                  |                     50 |
+| Berries | Blueberry, Strawberry, Cranberry |                    150 |
+| Grain   | Corn, Buckwheat, Barley          |                    200 |
+
+And let’s add another list in the end:
+
+- Leaves
+- Berries
+- Grain
+
+And here my listing in code:
+
+```
+Leaves
+
+Berries
+Grain
+```
diff --git a/tests/data/md/mixed.md b/tests/data/md/mixed.md
new file mode 100644
index 00000000..470ffb03
--- /dev/null
+++ b/tests/data/md/mixed.md
@@ -0,0 +1,54 @@
+# Title
+
+Some text
+
+## Famous ducks
+
+Here is a table:
+
+<table>
+  <tr>
+    <th>Character</th>
+    <th>Name in German</th>
+    <th>Name in French</th>
+    <th>Name in Italian</th>
+  </tr>
+  <tr>
+    <td>Scrooge McDuck</td>
+    <td>Dagobert Duck</td>
+    <td>Balthazar Picsou</td>
+    <td>Paperone</td>
+  </tr>
+  <tr>
+    <td>Huey</td>
+    <td>Tick</td>
+    <td>Riri</td>
+    <td>Qui</td>
+  </tr>
+  <tr>
+    <td>Dewey</td>
+    <td>Trick</td>
+    <td>Fifi</td>
+    <td>Quo</td>
+  </tr>
+  <tr>
+    <td>Louie</td>
+    <td>Track</td>
+    <td>Loulou</td>
+    <td>Qua</td>
+  </tr>
+</table>
+
+And here is more HTML:
+
+<p>Some paragraph.</p>
+
+<div>
+    <p>Now a div — almost there...</p>
+    <ul>
+        <li>foo</li>
+        <li>bar</li>
+    </ul>
+</div>
+
+The end!
diff --git a/tests/data/picture_classification.pdf b/tests/data/picture_classification.pdf
new file mode 100644
index 00000000..230f74fd
Binary files /dev/null and b/tests/data/picture_classification.pdf differ
diff --git a/tests/data/xlsx/test-01.xlsx b/tests/data/xlsx/test-01.xlsx
index ab75b72d..ea35b723 100644
Binary files a/tests/data/xlsx/test-01.xlsx and b/tests/data/xlsx/test-01.xlsx differ
diff --git a/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json b/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json
index 426ee19d..dcaf59cd 100644
--- a/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json
+++ b/tests/data_scanned/groundtruth/docling_v1/ocr_test.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
\ No newline at end of file
diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json b/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json
index 426ee19d..dcaf59cd 100644
--- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json
+++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.pages.json
@@ -1 +1 @@
-[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715733528137207, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
\ No newline at end of file
+[{"page_no": 0, "size": {"width": 595.201171875, "height": 841.9216918945312}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null}, "assembled": {"elements": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "body": [{"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 69.6796630536824, "t": 76.99999977896756, "r": 504.8720051760782, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}, "confidence": 0.9715732336044312, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 73.34702132031646, "t": 76.99999977896756, "r": 503.64955224479564, "b": 97.99999977896755, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 69.6796630536824, "t": 104.00000011573796, "r": 504.8720051760782, "b": 124.83139494707741, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 71.84193505100733, "t": 129.797125232046, "r": 153.088934155825, "b": 152.90926970226084, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "headers": []}}]
\ No newline at end of file
diff --git a/tests/test_backend_docling_json.py b/tests/test_backend_docling_json.py
new file mode 100644
index 00000000..a38d9da1
--- /dev/null
+++ b/tests/test_backend_docling_json.py
@@ -0,0 +1,58 @@
+"""Test methods in module docling.backend.json.docling_json_backend.py."""
+
+from io import BytesIO
+from pathlib import Path
+
+import pytest
+from pydantic import ValidationError
+
+from docling.backend.json.docling_json_backend import DoclingJSONBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import DoclingDocument, InputDocument
+
+GT_PATH: Path = Path("./tests/data/groundtruth/docling_v2/2206.01062.json")
+
+
+def test_convert_valid_docling_json():
+    """Test ingestion of valid Docling JSON."""
+    cls = DoclingJSONBackend
+    path_or_stream = GT_PATH
+    in_doc = InputDocument(
+        path_or_stream=path_or_stream,
+        format=InputFormat.JSON_DOCLING,
+        backend=cls,
+    )
+    backend = cls(
+        in_doc=in_doc,
+        path_or_stream=path_or_stream,
+    )
+    assert backend.is_valid()
+
+    act_doc = backend.convert()
+    act_data = act_doc.export_to_dict()
+
+    exp_doc = DoclingDocument.load_from_json(GT_PATH)
+    exp_data = exp_doc.export_to_dict()
+
+    assert act_data == exp_data
+
+
+def test_invalid_docling_json():
+    """Test ingestion of invalid Docling JSON."""
+    cls = DoclingJSONBackend
+    path_or_stream = BytesIO(b"{}")
+    in_doc = InputDocument(
+        path_or_stream=path_or_stream,
+        format=InputFormat.JSON_DOCLING,
+        backend=cls,
+        filename="foo",
+    )
+    backend = cls(
+        in_doc=in_doc,
+        path_or_stream=path_or_stream,
+    )
+
+    assert not backend.is_valid()
+
+    with pytest.raises(ValidationError):
+        backend.convert()
diff --git a/tests/test_backend_html.py b/tests/test_backend_html.py
index 3bd27242..a4deb212 100644
--- a/tests/test_backend_html.py
+++ b/tests/test_backend_html.py
@@ -6,6 +6,7 @@ from docling.backend.html_backend import HTMLDocumentBackend
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import (
     ConversionResult,
+    DoclingDocument,
     InputDocument,
     SectionHeaderItem,
 )
@@ -44,7 +45,7 @@ def get_html_paths():
     # Define the directory you want to search
     directory = Path("./tests/data/html/")
 
-    # List all PDF files in the directory and its subdirectories
+    # List all HTML files in the directory and its subdirectories
     html_files = sorted(directory.rglob("*.html"))
     return html_files
 
diff --git a/tests/test_backend_markdown.py b/tests/test_backend_markdown.py
new file mode 100644
index 00000000..caa94d9f
--- /dev/null
+++ b/tests/test_backend_markdown.py
@@ -0,0 +1,35 @@
+from pathlib import Path
+
+from docling.backend.md_backend import MarkdownDocumentBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import InputDocument
+
+
+def test_convert_valid():
+    fmt = InputFormat.MD
+    cls = MarkdownDocumentBackend
+
+    test_data_path = Path("tests") / "data"
+    relevant_paths = sorted((test_data_path / "md").rglob("*.md"))
+    assert len(relevant_paths) > 0
+
+    for in_path in relevant_paths:
+        gt_path = test_data_path / "groundtruth" / "docling_v2" / f"{in_path.name}.md"
+
+        in_doc = InputDocument(
+            path_or_stream=in_path,
+            format=fmt,
+            backend=cls,
+        )
+        backend = cls(
+            in_doc=in_doc,
+            path_or_stream=in_path,
+        )
+        assert backend.is_valid()
+
+        act_doc = backend.convert()
+        act_data = act_doc.export_to_markdown()
+
+        with open(gt_path, "r", encoding="utf-8") as f:
+            exp_data = f.read().rstrip()
+        assert act_data == exp_data
diff --git a/tests/test_backend_msexcel.py b/tests/test_backend_msexcel.py
index d13959ed..f33dffa2 100644
--- a/tests/test_backend_msexcel.py
+++ b/tests/test_backend_msexcel.py
@@ -2,13 +2,8 @@ import json
 import os
 from pathlib import Path
 
-from docling.backend.msword_backend import MsWordDocumentBackend
 from docling.datamodel.base_models import InputFormat
-from docling.datamodel.document import (
-    ConversionResult,
-    InputDocument,
-    SectionHeaderItem,
-)
+from docling.datamodel.document import ConversionResult, DoclingDocument
 from docling.document_converter import DocumentConverter
 
 GENERATE = False
@@ -53,7 +48,7 @@ def test_e2e_xlsx_conversions():
     converter = get_converter()
 
     for xlsx_path in xlsx_paths:
-        # print(f"converting {xlsx_path}")
+        print(f"converting {xlsx_path}")
 
         gt_path = (
             xlsx_path.parent.parent / "groundtruth" / "docling_v2" / xlsx_path.name
diff --git a/tests/test_backend_msword.py b/tests/test_backend_msword.py
index 24db6775..86bd837d 100644
--- a/tests/test_backend_msword.py
+++ b/tests/test_backend_msword.py
@@ -6,6 +6,7 @@ from docling.backend.msword_backend import MsWordDocumentBackend
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import (
     ConversionResult,
+    DoclingDocument,
     InputDocument,
     SectionHeaderItem,
 )
@@ -68,7 +69,6 @@ def verify_export(pred_text: str, gtfile: str):
         with open(gtfile, "r") as fr:
             true_text = fr.read()
 
-        assert pred_text == true_text, "pred_itxt==true_itxt"
         return pred_text == true_text
 
 
@@ -100,3 +100,7 @@ def test_e2e_docx_conversions():
 
         pred_json: str = json.dumps(doc.export_to_dict(), indent=2)
         assert verify_export(pred_json, str(gt_path) + ".json"), "export to json"
+
+        if docx_path.name == "word_tables.docx":
+            pred_html: str = doc.export_to_html()
+            assert verify_export(pred_html, str(gt_path) + ".html"), "export to html"
diff --git a/tests/test_backend_patent_uspto.py b/tests/test_backend_patent_uspto.py
index 466568ac..21bc88c5 100644
--- a/tests/test_backend_patent_uspto.py
+++ b/tests/test_backend_patent_uspto.py
@@ -3,23 +3,16 @@
 import json
 import logging
 import os
-import unittest
 from pathlib import Path
 from tempfile import NamedTemporaryFile
 
 import pytest
-import yaml
 from docling_core.types import DoclingDocument
 from docling_core.types.doc import DocItemLabel, TableData, TextItem
 
 from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend, XmlTable
 from docling.datamodel.base_models import InputFormat
-from docling.datamodel.document import (
-    ConversionResult,
-    InputDocument,
-    SectionHeaderItem,
-)
-from docling.document_converter import DocumentConverter
+from docling.datamodel.document import InputDocument
 
 GENERATE: bool = True
 DATA_PATH: Path = Path("./tests/data/uspto/")
diff --git a/tests/test_backend_pptx.py b/tests/test_backend_pptx.py
index 4c3872be..f4799a83 100644
--- a/tests/test_backend_pptx.py
+++ b/tests/test_backend_pptx.py
@@ -3,7 +3,7 @@ import os
 from pathlib import Path
 
 from docling.datamodel.base_models import InputFormat
-from docling.datamodel.document import ConversionResult
+from docling.datamodel.document import ConversionResult, DoclingDocument
 from docling.document_converter import DocumentConverter
 
 GENERATE = False
diff --git a/tests/test_backend_pubmed.py b/tests/test_backend_pubmed.py
index 4476bd24..8481c3dd 100644
--- a/tests/test_backend_pubmed.py
+++ b/tests/test_backend_pubmed.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 from io import BytesIO
 from pathlib import Path
diff --git a/tests/test_code_formula.py b/tests/test_code_formula.py
new file mode 100644
index 00000000..05e87246
--- /dev/null
+++ b/tests/test_code_formula.py
@@ -0,0 +1,66 @@
+from pathlib import Path
+
+from docling_core.types.doc import CodeItem, TextItem
+from docling_core.types.doc.labels import CodeLanguageLabel, DocItemLabel
+
+from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
+from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import ConversionResult
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
+
+
+def get_converter():
+
+    pipeline_options = PdfPipelineOptions()
+    pipeline_options.generate_page_images = True
+
+    pipeline_options.do_ocr = False
+    pipeline_options.do_table_structure = False
+    pipeline_options.do_code_enrichment = True
+    pipeline_options.do_formula_enrichment = True
+
+    converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(
+                backend=DoclingParseV2DocumentBackend,
+                pipeline_cls=StandardPdfPipeline,
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+
+    return converter
+
+
+def test_code_and_formula_conversion():
+    pdf_path = Path("tests/data/code_and_formula.pdf")
+    converter = get_converter()
+
+    print(f"converting {pdf_path}")
+
+    doc_result: ConversionResult = converter.convert(pdf_path)
+
+    results = doc_result.document.texts
+
+    code_blocks = [el for el in results if isinstance(el, CodeItem)]
+    assert len(code_blocks) == 1
+
+    gt = 'public static void print() {\n    System.out.println("Java Code");\n}'
+
+    predicted = code_blocks[0].text.strip()
+    assert predicted == gt, f"mismatch in text {predicted=}, {gt=}"
+    assert code_blocks[0].code_language == CodeLanguageLabel.JAVA
+
+    formula_blocks = [
+        el
+        for el in results
+        if isinstance(el, TextItem) and el.label == DocItemLabel.FORMULA
+    ]
+    assert len(formula_blocks) == 1
+
+    gt = "a ^ { 2 } + 8 = 1 2"
+    predicted = formula_blocks[0].text
+    assert predicted == gt, f"mismatch in text {predicted=}, {gt=}"
diff --git a/tests/test_document_picture_classifier.py b/tests/test_document_picture_classifier.py
new file mode 100644
index 00000000..0ad87e96
--- /dev/null
+++ b/tests/test_document_picture_classifier.py
@@ -0,0 +1,81 @@
+from pathlib import Path
+
+from docling_core.types.doc import PictureClassificationData
+
+from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.document import ConversionResult
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
+
+
+def get_converter():
+
+    pipeline_options = PdfPipelineOptions()
+    pipeline_options.generate_page_images = True
+
+    pipeline_options.do_ocr = False
+    pipeline_options.do_table_structure = False
+    pipeline_options.do_code_enrichment = False
+    pipeline_options.do_formula_enrichment = False
+    pipeline_options.do_picture_classification = True
+    pipeline_options.generate_picture_images = True
+    pipeline_options.images_scale = 2
+
+    converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(
+                backend=DoclingParseV2DocumentBackend,
+                pipeline_cls=StandardPdfPipeline,
+                pipeline_options=pipeline_options,
+            )
+        }
+    )
+
+    return converter
+
+
+def test_picture_classifier():
+    pdf_path = Path("tests/data/picture_classification.pdf")
+    converter = get_converter()
+
+    print(f"converting {pdf_path}")
+
+    doc_result: ConversionResult = converter.convert(pdf_path)
+
+    results = doc_result.document.pictures
+
+    assert len(results) == 2
+
+    res = results[0]
+    assert len(res.annotations) == 1
+    assert type(res.annotations[0]) == PictureClassificationData
+    classification_data = res.annotations[0]
+    assert classification_data.provenance == "DocumentPictureClassifier"
+    assert (
+        len(classification_data.predicted_classes) == 16
+    ), "Number of predicted classes is not equal to 16"
+    confidences = [pred.confidence for pred in classification_data.predicted_classes]
+    assert confidences == sorted(
+        confidences, reverse=True
+    ), "Predictions are not sorted in descending order of confidence"
+    assert (
+        classification_data.predicted_classes[0].class_name == "bar_chart"
+    ), "The prediction is wrong for the bar chart image."
+
+    res = results[1]
+    assert len(res.annotations) == 1
+    assert type(res.annotations[0]) == PictureClassificationData
+    classification_data = res.annotations[0]
+    assert classification_data.provenance == "DocumentPictureClassifier"
+    assert (
+        len(classification_data.predicted_classes) == 16
+    ), "Number of predicted classes is not equal to 16"
+    confidences = [pred.confidence for pred in classification_data.predicted_classes]
+    assert confidences == sorted(
+        confidences, reverse=True
+    ), "Predictions are not sorted in descending order of confidence"
+    assert (
+        classification_data.predicted_classes[0].class_name == "map"
+    ), "The prediction is wrong for the bar chart image."
diff --git a/tests/test_e2e_conversion.py b/tests/test_e2e_conversion.py
index d6753b04..0c572595 100644
--- a/tests/test_e2e_conversion.py
+++ b/tests/test_e2e_conversion.py
@@ -3,7 +3,7 @@ from pathlib import Path
 from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import ConversionResult
-from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.datamodel.pipeline_options import AcceleratorDevice, PdfPipelineOptions
 from docling.document_converter import DocumentConverter, PdfFormatOption
 
 from .verify_utils import verify_conversion_result_v1, verify_conversion_result_v2
@@ -28,6 +28,7 @@ def get_converter():
     pipeline_options.do_ocr = False
     pipeline_options.do_table_structure = True
     pipeline_options.table_structure_options.do_cell_matching = True
+    pipeline_options.accelerator_options.device = AcceleratorDevice.CPU
 
     converter = DocumentConverter(
         format_options={
diff --git a/tests/test_e2e_ocr_conversion.py b/tests/test_e2e_ocr_conversion.py
index 73a943af..62e4c855 100644
--- a/tests/test_e2e_ocr_conversion.py
+++ b/tests/test_e2e_ocr_conversion.py
@@ -6,6 +6,7 @@ from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import (
+    AcceleratorDevice,
     EasyOcrOptions,
     OcrMacOptions,
     OcrOptions,
@@ -37,6 +38,7 @@ def get_converter(ocr_options: OcrOptions):
     pipeline_options.do_table_structure = True
     pipeline_options.table_structure_options.do_cell_matching = True
     pipeline_options.ocr_options = ocr_options
+    pipeline_options.accelerator_options.device = AcceleratorDevice.CPU
 
     converter = DocumentConverter(
         format_options={
@@ -57,20 +59,27 @@ def test_e2e_conversions():
         EasyOcrOptions(),
         TesseractOcrOptions(),
         TesseractCliOcrOptions(),
-        RapidOcrOptions(),
         EasyOcrOptions(force_full_page_ocr=True),
         TesseractOcrOptions(force_full_page_ocr=True),
+        TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]),
         TesseractCliOcrOptions(force_full_page_ocr=True),
-        RapidOcrOptions(force_full_page_ocr=True),
+        TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]),
     ]
 
+    # rapidocr is only available for Python >=3.6,<3.13
+    if sys.version_info < (3, 13):
+        engines.append(RapidOcrOptions())
+        engines.append(RapidOcrOptions(force_full_page_ocr=True))
+
     # only works on mac
     if "darwin" == sys.platform:
         engines.append(OcrMacOptions())
         engines.append(OcrMacOptions(force_full_page_ocr=True))
 
     for ocr_options in engines:
-        print(f"Converting with ocr_engine: {ocr_options.kind}")
+        print(
+            f"Converting with ocr_engine: {ocr_options.kind}, language: {ocr_options.lang}"
+        )
         converter = get_converter(ocr_options=ocr_options)
         for pdf_path in pdf_paths:
             print(f"converting {pdf_path}")
diff --git a/tests/test_input_doc.py b/tests/test_input_doc.py
index 8b084667..efecb81e 100644
--- a/tests/test_input_doc.py
+++ b/tests/test_input_doc.py
@@ -4,6 +4,7 @@ from pathlib import Path
 from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend
 from docling.datamodel.base_models import DocumentStream, InputFormat
 from docling.datamodel.document import InputDocument, _DocumentConversionInput
+from docling.datamodel.settings import DocumentLimits
 
 
 def test_in_doc_from_valid_path():
@@ -39,6 +40,40 @@ def test_in_doc_from_invalid_buf():
     assert doc.valid == False
 
 
+def test_in_doc_with_page_range():
+    test_doc_path = Path("./tests/data/2206.01062.pdf")
+    limits = DocumentLimits()
+    limits.page_range = (1, 10)
+
+    doc = InputDocument(
+        path_or_stream=test_doc_path,
+        format=InputFormat.PDF,
+        backend=PyPdfiumDocumentBackend,
+        limits=limits,
+    )
+    assert doc.valid == True
+
+    limits.page_range = (9, 9)
+
+    doc = InputDocument(
+        path_or_stream=test_doc_path,
+        format=InputFormat.PDF,
+        backend=PyPdfiumDocumentBackend,
+        limits=limits,
+    )
+    assert doc.valid == True
+
+    limits.page_range = (11, 12)
+
+    doc = InputDocument(
+        path_or_stream=test_doc_path,
+        format=InputFormat.PDF,
+        backend=PyPdfiumDocumentBackend,
+        limits=limits,
+    )
+    assert doc.valid == False
+
+
 def test_guess_format(tmp_path):
     """Test docling.datamodel.document._DocumentConversionInput.__guess_format"""
     dci = _DocumentConversionInput(path_or_stream_iterator=[])
@@ -124,6 +159,25 @@ def test_guess_format(tmp_path):
     doc_path.write_text("xyz", encoding="utf-8")
     assert dci._guess_format(doc_path) == None
 
+    # Valid Docling JSON
+    test_str = '{"name": ""}'
+    stream = DocumentStream(name="test.json", stream=BytesIO(f"{test_str}".encode()))
+    assert dci._guess_format(stream) == InputFormat.JSON_DOCLING
+    doc_path = temp_dir / "test.json"
+    doc_path.write_text(test_str, encoding="utf-8")
+    assert dci._guess_format(doc_path) == InputFormat.JSON_DOCLING
+
+    # Non-Docling JSON
+    # TODO: Docling JSON is currently the single supported JSON flavor and the pipeline
+    # will try to validate *any* JSON (based on suffix/MIME) as Docling JSON; proper
+    # disambiguation seen as part of https://github.com/DS4SD/docling/issues/802
+    test_str = "{}"
+    stream = DocumentStream(name="test.json", stream=BytesIO(f"{test_str}".encode()))
+    assert dci._guess_format(stream) == InputFormat.JSON_DOCLING
+    doc_path = temp_dir / "test.json"
+    doc_path.write_text(test_str, encoding="utf-8")
+    assert dci._guess_format(doc_path) == InputFormat.JSON_DOCLING
+
 
 def _make_input_doc(path):
     in_doc = InputDocument(
diff --git a/tests/test_options.py b/tests/test_options.py
index 8d861e48..1dd3bbc8 100644
--- a/tests/test_options.py
+++ b/tests/test_options.py
@@ -105,6 +105,20 @@ def test_e2e_conversions(test_doc_path):
         assert doc_result.status == ConversionStatus.SUCCESS
 
 
+def test_page_range(test_doc_path):
+    converter = DocumentConverter()
+    doc_result: ConversionResult = converter.convert(test_doc_path, page_range=(9, 9))
+
+    assert doc_result.status == ConversionStatus.SUCCESS
+    assert doc_result.input.page_count == 9
+    assert doc_result.document.num_pages() == 1
+
+    doc_result: ConversionResult = converter.convert(
+        test_doc_path, page_range=(10, 10), raises_on_error=False
+    )
+    assert doc_result.status == ConversionStatus.FAILURE
+
+
 def test_ocr_coverage_threshold(test_doc_path):
     pipeline_options = PdfPipelineOptions()
     pipeline_options.do_ocr = True